Patent Application
20250198820
This disclosure generally relates to measurement systems for conveyors. In particular, this disclosure relates to a measurement system configured to implement sensor fusion to determine mass material rates for conveyors.
Asphalt-paved roadways often facilitate substantial amounts of vehicular travel. When exposed to loading forces associated with vehicular travel as well as weather elements, such as temperature variation and moisture exposure, among other elements, roadways become worn or otherwise in a state of disrepair. Consequently, asphalt roadway surfaces periodically need to be replaced. The worn roadway surface must be removed before it can be replaced. A cold planer machine, also referred to as an asphalt milling machine, is used to break apart and remove asphalt layers from a roadway to prepare the roadway for repaving.
Cold planers often include a frame supported by multiple track or wheel elements that are driven by an engine. The cold planer further includes a milling assembly including one or more milling drums that rotate underneath the frame to engage cutting tools with the surface of the roadway to break apart (e.g., mill) the surface of the roadway. Broken-up pieces of roadway surface are conveyed away from the cold planer via a series of conveyor assemblies. A first conveyor assembly transfers the broken-up material from an underside of the cold planer to a second conveyor assembly, and the second conveyor assembly will convey the material away from the cold planer into a bed of a truck, for example. The truck will move the broken-up material from the roadway to some other location, such as a reclamation facility where the broken-up material can be reused as aggregate for new asphalt or otherwise recycled. Generally, after material is deposited into the bed of a first truck, a second truck will be positioned under the second conveyor assembly to receive additional material. This process is repeated until substantially all of the broken-up material is removed from the roadway.
In order to make efficient use of trucks, it may be desirable to avoid underfilling a truck bed as this would require more truckloads to remove the broken-up material. On the other hand, it may be desirable to avoid over-filling trucks as trucks with overweight loads can incur substantial financial penalties. To ensure trucks are neither over-filled nor under-filled, it is necessary to monitor an amount (e.g., a mass) of material conveyed from the cold planer into a truck bed. Accurately measuring a mass flow rate of material conveyed from the cold planer to the truck bed is difficult as the mass flow rate varies as a tension on the conveyor belt changes, as the conveyor belt slips, and/or as environmental parameters (e.g., temperature or moisture) change. Without reliable mass flow rate measurements, operators may resort to underfilling trucks to mitigate the risk of financial penalty, resulting in suboptimal utilization of trucks hauling broken-up roadway material.
One aspect of the present disclosure is related to a cold planer. The cold planer includes a conveyor system including a conveyor belt configured to move material, a sensor configured to collect information regarding operation of the conveyor system, and one or more processing circuits in communication with the conveyor system and the sensor. The one or more processing circuits configured to detect, based on one or more operational parameters of the cold planer, an operation of the cold planer. The operation includes a flow of material on the conveyor belt. The one or more processing circuits also configured to receive, responsive to detection of the flow of the material, from the sensor, a plurality of information corresponding to the cold planer, the plurality of information includes a first set of information corresponding to a first aspect of the cold planer and a second set of information corresponding to a second aspect of the cold planer. The one or more processing circuits also configured to identify, based on the first set of information, a first status of the cold planer. The one or more processing circuits also configured to identify, based on the second set of information, a second status of the cold planer. The one or more processing circuits also configured to combine the first set of information and the second set of information to create a fused set of information, the fused set of information includes the first status of the cold planer and the second status of the cold planer. The one or more processing circuits also configured to generate, using a machine learning model stored in memory, based on the fused set of information, a mass flow rate of the material on the conveyor belt.
One aspect of the present disclosure is related to one or more processing circuits. The one or more processing circuits in communication with a cold planer. The one or more processing circuits configured to detect, based on one or more operational parameters of the cold planer, an operation of the cold planer. The operation includes a flow of material on a conveyor belt of the cold planer. The one or more processing circuits also configured to receive, responsive to detection of the flow of the material, from a plurality of sensors, a plurality of information corresponding to the cold planer, the plurality of information includes a first set of information corresponding to a first aspect of the cold planer and a second set of information corresponding to a second aspect of the cold planer. The one or more processing circuits also configured to identify, based on the first set of information, a first status of the cold planer. The one or more processing circuits also configured to identify, based on the second set of information, a second status of the cold planer. The one or more processing circuits also configured to combine the first set of information and the second set of information to create a fused set of information, the fused set of information includes the first status of the cold planer and the second status of the cold planer. The one or more processing circuits also configured to generate, using a machine learning model stored in memory, based on the fused set of information, a mass flow rate of the material on the conveyor belt.
Another aspect of the present disclosure is related to a method. The method includes detecting, by one or more processing circuits in communication with a cold planer, based on one or more operational parameters of the cold planer, an operation of the cold planer. The operation includes a flow of material on a conveyor belt of the cold planer. The method also includes receiving, by the one or more processing circuits, responsive to detection of the flow of the material, from a plurality of sensors, a plurality of information corresponding to the cold planer, the plurality of information includes a first set of information corresponding to a first aspect of the cold planer and a second set of information corresponding to a second aspect of the cold planer. The method also includes identifying, by the one or more processing circuits, based on the first set of information, a first status of the cold planer. The method also includes identifying, by the one or more processing circuits, based on the second set of information, a second status of the cold planer. The method also includes combining, by the one or more processing circuits, the first set of information and the second set of information to create a fused set of information, the fused set of information includes the first status of the cold planer and the second status of the cold planer. The method also includes generating, by the one or more processing circuits, using a machine learning model stored in memory, based on the fused set of information, a mass flow rate of the material on the conveyor belt.
Referring to
The cold planer 10 includes a first conveyor system 20 and a second conveyor system 22. The first conveyor system 20 includes a conveyor belt coupled with at least one rotatable roller and a motor. The motor is operatively coupled to the conveyor belt (e.g., via a roller) to drive the conveyor belt around the roller. The movement of the conveyor belt is configured to convey material away from the milling assembly 16. For example, the milling assembly 16 can mill (e.g., cut, break apart) the surface 13 to create milled material (e.g., broken-up asphalt material) that is conveyed away from the milling assembly 16 via the first conveyor system 20. The amount of milled material conveyed from the milling assembly 16 via the conveyor system 20 depends at least in part on a depth at which the cutting teeth of the milling assembly 16 cut into the surface 13. For example, if the actuators 18 are actuated to lower the frame 11 of the cold planer 10 towards the surface 13, the milling assembly 16 will correspondingly be positioned lower such that the cutting teeth of the milling assembly 16 will cut the surface 13 to a greater depth, resulting in a greater amount (e.g., volume) of milled material being removed from the surface 13.
The first conveyor system 20 is configured to provide the milled material to the second conveyor system 22. The conveyor system 22 includes a proximal end 24 positioned near a leading end of the cold planer 10 and a distal end 26 positioned away from the leading end of the cold planer 10. The conveyor system 22 is pivotally connected at a leading end to frame 11 such that an angle of the conveyor system 22 can be varied relative to surface 13 or the frame 11. For example, the distal end 26 of the conveyor system 22 can be raised or lowered relative to the proximal end 24 to adjust the angle of the conveyor system 22 relative to a surface 13 or to the frame 11. The cold planer 10 includes an angle sensor 41 coupled with the conveyor system 22 and configured to determine an angle of the conveyor system 22 relative to the frame 11 of the cold planer 10 or relative to the surface 13. For example, the angle sensor 41 could be an inclinometer, an optical sensor, an encoder, or some other sensor configured to determine an angle of the conveyor system 22. The conveyor system 22 includes a conveyor belt 40 driven by a motor 38 and configured to move in a conveyance direction 28 about a plurality of rotatable rollers 42. The conveyor system 22 includes the motor 38 driving the conveyor belt 40 along the rollers 42 from the distal end 26 of the conveyor system 22. Specifically, the distal end 26 of the conveyor system 22 can include a roller 42 or a drum that is driven by the motor 38 (e.g., a drive roller 42A), and the proximal end 24 can include a roller 42 that rotates as the motor 38 drives the conveyor belt 40. Accordingly, the conveyor system 22 includes the conveyor belt 40 driven by the motor 38 at one location and supported by rollers 42 at multiple other locations (e.g., idle rollers).
Milled material (e.g., broken-up asphalt material) is conveyed in the conveyance direction 28 as the conveyor belt 40 is driven by the motor 38. As depicted in
The cold planer 10 can be employed at a worksite, (e.g., a roadway) to perform a roadway milling operation. In an example milling operation, the cold planer 10 milling assembly 16 can be used to break apart (e.g., mill, cut) the surface 13 of the roadway. The conveyor belt of the first conveyor system 20 transfers milled material from the milling assembly 16 and to the second conveyor system 22. The motor 38 of the second conveyor system 22 rotates in the direction 39 to convey the milled material in the conveyance direction 28. The milled material is conveyed from (e.g., off of) the distal end 26 of the conveyor system 22 in the conveyance direction 28 and into a bed 36 of a truck 34. The truck 34 can be a haul truck, such as a dump truck (e.g., an articulating dump truck), or some other truck having a bed, hopper, or other vessel configured to collect milled material. Milled material is deposited in the bed 36 of the truck 34 via the conveyor system 22. Once the bed 36 of the truck 34 is filled to a desirable level (e.g., with a desirable mass milled material), the truck 34 will move the milled material from the roadway to some other location, such as a reclamation facility where the broken-up material can be reused as aggregate for new asphalt or otherwise recycled. During the example milling operation, milled material can be further conveyed into the bed 36 of a second truck 34 that can be positioned proximate the distal end 26 of the conveyor system 22. This process is repeated until the milled material from the surface 13 of the roadway is removed, for example.
Still referring to
The cold planer 10 includes a controller 32. The controller 32, which is discussed in detail below with reference to
As depicted in
As noted above, the motor 38 drives the conveyor belt 40 as the motor 38 or some element of the motor 38 or operatively coupled with the motor 38 (e.g., a roller, a shaft, or other drive mechanism) rotates in the direction 39. The motor 38 generates a force (e.g., a torsional force) that is imparted on the conveyor belt 40 to cause that conveyor belt 40 to move. The motor 38 imparts the force on the conveyor belt 40 via a friction force. Specifically, friction between the belt 40 and the motor 38 drives the conveyor belt 40 (and milled material supported by the conveyor belt 40) as the motor 38 is operated. If the friction force between the motor 38 and the conveyor belt 40 is overcome, however, the conveyor belt 40 can “slip” relative to the motor 38. In instances where the conveyor belt 40 slips, the motor 38 will be operating to drive the conveyor belt 40 at a desired conveyance speed, but the conveyor belt 40 will be moving at an actual conveyance speed that is less than the desired conveyance speed. The conveyor belt 40 can slip relative to the motor 38, for example, as an underside of the conveyor belt 40 becomes worn, as a temperature of the conveyor belt 40 varies, as a speed of the motor 38 increases (e.g., as the torsional force momentarily exceeds the friction force), as tension in the conveyor belt 40 decreases, or for other reasons.
The rollers 42 support the conveyor belt 40 as the motor 38 drives the conveyor belt 40. The rollers 42 are free rotate as the conveyor belt 40 moves. In other words, the rollers 42 are not driving the conveyor belt 40 but are instead driven via movement of the conveyor belt 40. The conveyor belt 40 rides along the rollers 42 that are supported by the frame 44 of the conveyor system 22. As the conveyor belt 40 moves, a friction force is imparted by the conveyor belt 40 on the rollers 42, causing the rollers 42 to roll in the direction 39 as the conveyor belt 40 moves. The friction between an outer surface of the roller 42 and an underside of the conveyor belt 40 causes the rollers 42 to move.
As depicted in
As depicted in
In embodiments where the speed sensor 46 is configured to measure the speed of the conveyor belt 40, such as when the speed sensor 46 is a rotary encoder having a wheel 47 that rides along the conveyor belt 40, the speed sensor 46 can obtain a speed measurement from a tight side of the conveyor belt 40, rather than on a slack side of the conveyor belt 40. As discussed above, the conveyor belt 40 is configured to roll along the rollers 42 and be driven by the motor 38. Specifically, the conveyor belt 40 forms a continuous loop having a top side and a bottom side with the rollers 42 the motor 38 (or some driving component of the motor 38) positioned between the top side and the bottom side. As the motor 38 drives the conveyor belt 40 around the rollers 42, one of the top side or the bottom side of the conveyor belt 40 is a tight side (e.g., a side in tension) while the other side is a slack side (e.g., a compressed side). For example, in the arrangement depicted in
As depicted in
As depicted in
Referring now to
Referring now to
The controller 32 is communicably coupled with the angle sensor 41, the operator interface 31, the load cell 45, the motor 38, and the speed sensor 46, among other components of the cold planer 10. Data recorded by the angle sensor 41, the load cell 45, or the speed sensor 46 are transmitted to and received by the controller 32, where such data can be saved, analyzed, or otherwise used. Likewise, the controller 32 is configured to receive data, commands, or other information transmitted from the operator interface 31.
The communication interface 58 of the controller 32 is configured to enable the controller 32 to exchange information over a wired or wireless network. In some examples, the communication interface 58 can include program logic that facilitates connection of the controller 32 to a network (e.g., a cellular network, Wi-Fi, Bluetooth, radio, etc.) for wireless communication. For example, the communication interface 58 can support communications between the controller 32 and other systems, such as a remote monitoring computing system (e.g., a cloud-based fleet management service, or some other remotely located computing system). In instances where the communication interface 58 is configured for wireless communication, the communication interface 58 can include a cellular modem, a Bluetooth transceiver, a radio-frequency identification (RFID) transceiver, and a near-field communication (NFC) transmitter, or some other short-range wireless transceiver. In some embodiments, the communication interface 58 includes the hardware and machine-readable media sufficient to support communication over multiple channels of data communication simultaneously or separately.
The communication interface 58 is configured to facilitate the transmission of data and commands between the controller 32 and various other systems or devices (e.g., the angle sensor 41, the operator interface 31, the load cell 45, the motor 38, and the speed sensor 46, or some other system or device associated with the cold planer 10). In such embodiments, the communication interface 58 can communicate with other systems or devices via an internal communications network, such as a controller area network (CAN bus) or another vehicle electronic communications protocol. Each of the angle sensor 41, the operator interface 31, the load cell 45, the motor 38, and the speed sensor 46 are communicably coupled to the controller 32 via the communication interface 58 using a CAN bus network or similar protocol.
The controller 32 includes the processing circuit 60, which further includes a processor 62 and a memory 64. The processor 62 is coupled to the memory 64. In some embodiments, the processor 62 can be a general purpose or specific purpose processor, an application specific integrated circuit (ASIC), one or more field programmable gate arrays (FPGAs), a group of processing components, or other suitable processing components. The processor 62 can be configured to execute computer code or instructions stored in the memory 64 or received from other computer readable media (e.g., CDROM, network storage, a remote server, etc.).
The memory 64 includes one or more devices (e.g., memory units, memory devices, storage devices, etc.) for storing data and/or computer code for completing and/or facilitating various processes. For example, the memory 64 can include random access memory (RAM), read-only memory (ROM), hard drive storage, temporary storage, non-volatile memory, flash memory, optical memory, or any other suitable memory for storing software objects and/or computer instructions. The memory 64 can include database components, object code components, script components, or any other type of information structure for supporting the various activities and information structures described in the present disclosure. The memory 64 is communicably connected to the processor 62 via processing circuit 60. The memory 64 includes computer code for executing (e.g., by the processor 62) one or more of the processes associated with the cold planer 10. Specifically, the memory 64 includes computer code for executing one or more of the processes associated with determining a mass flow rate of milled material conveyed along the conveyor belt 40 via the mass flow rate determination circuit 65. The memory 64 further includes computer code for executing one or more processes associated with determining an amount of belt slippage of the conveyor belt 40 via the belt slip determination circuit 66.
The mass flow rate determination circuit 65 of the controller 32 is configured to determine and control the material mass flow rate (e.g., the rate of material transfer into the truck 34), among other parameters. The mass flow rate of milled material conveyed by the conveyor system 22 and into the bed 36 of the truck 34 can be determined in a variety of ways. Specifically, the mass flow rate can be determined by: (1) a volume-based calculation method that considers an estimated volume of milled material removed by the milling assembly 16 as the cold planer 10 traverses the surface 13; (2) a conveyor drive power-based calculation that considers the drive power of the conveyor system 22 (e.g., the drive power of the motor 38) over time; and/or (3) a force and belt speed-based calculation method that considers a force applied to the conveyor belt 40 and a speed of the conveyor belt 40, among other parameters. In other embodiments, the mass flow rate determination circuit 65 can use other calculation methods.
The mass flow rate determination circuit 65 is configured to determine the mass flow rate of the milled material using the volume-based calculation method, which considers an estimated volume of milled material removed from the surface 13 by the milling assembly. To employ this calculation method, the mass flow rate determination circuit 65 uses known parameters of the cold planer 10 and measured operating parameters of the cold planer 10. For example, as noted above, the milling assembly 16 cuts into the surface 13 of the roadway to create milled material (e.g., broken up pieces of asphalt). More specifically, the milling assembly 16 cuts into the surface 13 to create a volume of milled material, where the volume of milled material can be expressed, for example, as a function of (i) a cutting depth into the surface 13, (ii) a width of the milling drum of the milling assembly 16 (e.g., a width of the milling drum having cutting teeth that engage the surface 13), and (iii) a distance traversed by the cold planer 10 along the surface 13 while the milling assembly 16 is cutting the surface 13 at the depth (i). The volume of milled material can be varied as the depth at which the cutting teeth cut the surface 13 is varied. The mass flow rate determination circuit 65 can determined the mass flow rate of milled material by computing the volume of material removed by the milling assembly 16 using these parameters over a given time interval. The mass flow rate determination circuit 65 can determine a mass of milled material removed by multiplying the volume of material removed by an estimated (e.g., known) or measured density of the material of the surface 13, for example.
Similarly, the mass flow rate determination circuit 65 can determine a volumetric flow rate of milled material removed by the milling assembly 16 of the cold planer 10. The volumetric flow rate of milled material can be expressed as a function of (i) the cutting depth into the surface 13, (ii) the width of the milling drum of the milling assembly 16 (e.g., a width of the milling drum having cutting teeth that engage the surface 13), and (iii) a rate at which the cold planer 10 is traversing the surface 13 while the milling assembly 16 is cutting the surface 13 at the depth (i). The mass flow rate determination circuit is configured to calculate the volumetric flow rate of milled material based on these parameters. The mass flow rate determination circuit 65 is configured to determine the mass flow rate of milled material using the volumetric flow rate of milled material. For example, the volumetric flow rate can be multiplied by an estimated (e.g., known) or measured density of the milled material to determine a mass flow rate of milled material.
The mass flow rate determination circuit 65 of the controller 32 is configured to determine the mass flow rate of milled material using the conveyor drive power-based calculation method. Specifically, the mass flow rate determination circuit 65 is configured to determine the mass flow rate of the milled material based on a drive power of the conveyor system 22 or parameters related to the drive power of the conveyor system 22. Specifically, the mass flow rate determination circuit 65 can receive a signal indicative of a drive power of the motor 38, such as a hydraulic pressure associated with the motor 38 (e.g., in embodiments where the motor 38 is a hydraulic motor) and a hydraulic temperature (e.g., a temperature of hydraulic oil associated with the motor 38). The mass flow rate determination circuit 65 is configured to receive a signal from the angle sensor 41 indicative of an angle of the conveyor system 22 relative to the surface 13 or relative to the frame 11 of the cold planer 10. In addition, the mass flow rate determination circuit 65 is configured to receive a signal indicative of the speed of the conveyor belt 40 and a signal indicative of a tension of the conveyor belt 40. Based on one or more of the hydraulic pressures associated with the motor 38, the temperature of the hydraulic fluid of the motor 38, the angle of the conveyor system 22, the speed of the conveyor belt 40, and the tension on the conveyor belt 40, the mass flow rate determination circuit 65 is configured to compute the mass flow rate of milled material conveyed by the conveyor belt 40. For example, an increased hydraulic pressure of the motor 38 or an increased tension on the conveyor belt 40 might indicate the presence of a large mass on the conveyor belt 40 and thus a relatively large mass flow rate of milled material. To the contrary, a reduced tension on the conveyor belt 40 or a reduced hydraulic pressure of the motor 38 might indicate a lesser mass on the conveyor belt 40 and thus a relatively small mass flow rate of milled material.
The mass flow rate determination circuit 65 of the controller 32 is configured to determine the mass flow rate of milled material being transferred by the conveyor belt 40 of the conveyor system 22 using the force and belt speed-based calculation method. Specifically, the mass flow rate determination circuit 65 is configured to determine the mass flow rate of the milled material based on a force applied to the conveyor belt 40 and a speed of the conveyor belt 40 or the motor 38. For example, the mass flow rate determination circuit 65 of the controller 32 is configured to receive a signal from load cell 45 that is indicative of the magnitude of a force (e.g., a force FN acting normal to an upper portion of the conveyor belt 40. The mass flow rate determination circuit 65 is further configured to receive a signal from the angle sensor 41 indicative of an angle of the conveyor system 22 relative to the surface 13 or the frame 11 of the cold planer 10. The mass flow rate determination circuit 65 calculates a force acting on the conveyor belt 40 based on the angle of the conveyor system 22 relative to the surface 13 or the frame 11 of the cold planer 10. The force acting on the conveyor belt 40 is indicative of an amount of force applied to conveyor belt 40 by the milled material. In one example, the mass flow rate determination circuit 65 can determine the force acting on the conveyor belt 40 by dividing the force acting on the load cell 45 by the cosine of the angle of inclination of the conveyor system 22. The force acting on the conveyor belt 40 can be divided by the gravitational acceleration to determine a mass of the milled material on the conveyor belt 40 at a particular moment in time. Finally, the mass flow rate determination circuit 65 of the controller 32 is configured to receive a signal indicative of a speed of the conveyor belt 40 or a speed of the motor 38. The mass flow rate determination circuit 65 is configured to determine a mass flow rate of milled material conveyed by the conveyor system 22 by dividing the calculated mass of the milled material on the conveyor belt 40 by the speed of the conveyor belt 40.
In some examples, the mass flow rate determination circuit 65 of the controller 32 continually determines the material mass flow rate of the milled material on the conveyor belt 40. In other examples, the mass flow rate determination circuit 65 periodically determines the material mass flow rate (e.g., at regular intervals). The mass flow rate determination circuit 65 is further configured to determine the total weight of material transferred by the conveyor system 22 and into the bed 36 of the truck 34. For example, the mass flow rate determination circuit 65 can be configured to multiply the material mass flow rate over a period of time, such as a period of milling time (e.g., a time interval over which the milling assembly 16 is engaged with the surface 13) and by summing the total over a period of conveying time (e.g., a time period over which the conveyor system 22 is actively conveying milled material into the bed 36 of the truck 34). The mass flow rate determination circuit 65 is configured to provide an indication of the mass flow rate of the milled material on the conveyor belt 40 to the operator interface 31. For example, the mass flow rate determination circuit 65 is can display a numeric value of the mass flow rate on a display screen of the operator interface 31. In other examples, the mass flow rate determination circuit 65 can provide an audible indication or some other visual indication of the mass flow rate to the operator of the cold planer 10, such as illuminating an LED that indicates that the mass flow rate of milled material is determined to be within a particular range. Similarly, the mass flow rate determination circuit 65 is configured to provide an indication of the total weight of milled material deposited into the bed 36 of the truck 34 to the operator of the cold planer 10 (or an operator of the truck 34).
In one example, the mass flow rate determination circuit 65 of the controller 32 is configured determine a mass flow rate of milled material conveyed by the conveyor system 22 based on one or more of the calculation methods discussed above, namely a volume-based calculation method, a conveyor drive power-based calculation method, or a force and belt speed-based calculation method. The mass flow rate determination circuit 65 is configured to selectively calculate the mass flow rate of milled material conveyed on the conveyor system 22 using a first calculation method under one set of circumstances and to selectively calculate the mass flow rate using a second calculation method under a second set of circumstances. For example, the mass flow rate determination circuit 65 can receive an indication that some parameter is unreliable such that mass flow rate calculations considering that parameter will likewise be unreliable. In such instances, the mass flow rate determination circuit 65 is configured to use another mass flow rate calculation method in order to achieve a more reliable mass flow rate calculation. For example, the mass flow rate determination circuit 65 can use a volume-based mass flow rate calculation method based on a determination that the conveyor belt 40 is slipping. The mass flow rate determination circuit 65 can further adjust or modify a mass flow rate calculation based on a signal or indication that some parameter is unreliable, within a certain range, or otherwise in some state. For example, the mass flow rate determination circuit 65 can determine that the conveyor belt 40 is slipping and can account for an amount (e.g., a percentage) of belt slippage in the mass flow rate calculation to bolster accuracy of the mass flow rate calculation.
The controller 32 includes a belt slip determination circuit 66. The belt slip determination circuit 66 is configured to determine an actual conveyance speed of the conveyor belt 40 that accounts for any slippage of the conveyor belt 40. As noted above, the conveyor belt 40 can “slip” relative to the motor 38 if the friction force between the motor 38 and the conveyor belt 40 is overcome, such as by the torque of the motor 38 for example. In instances where the conveyor belt 40 slips, the motor 38 will be operating to drive the conveyor belt 40 at a desired conveyance speed, but the conveyor belt 40 will be moving at the actual conveyance speed that is less than the desired conveyance speed. The conveyor belt 40 can slip relative to the motor 38, for example, as an underside of the conveyor belt 40 becomes worn, as a temperature of the conveyor belt 40 varies, as a speed of the motor 38 increases (e.g., as the torsional force momentarily exceeds the friction force), as tension in the conveyor belt 40 decreases, or for other reasons.
The belt slip determination circuit 66 is configured to receive a signal from the motor 38 indicative of an operating speed of the motor 38. The operating speed of the motor 38 can be a measure of the revolutions per minute (RPM) of the motor 38, a desired conveyance speed based on the RPM of the motor 38 and a diameter of a roller driven by the motor 38, or some other value. For example, the belt slip determination circuit 66 can determine a speed of the motor 38 based on the speed of rotation of a head pulley shaft or a speed ring gear of the motor 38. The belt slip determination circuit 66 is further configured to receive a signal from the speed sensor 46 indicative of the true speed of the conveyor belt 40. The speed sensor 46, whether embodied as a rotary encoder, an optical encoder, a magnetic encoder, or some other encoder, can measure a true speed of the conveyor belt 40 as it passes over a roller 42. For example, as depicted in
The mass flow rate determination circuit 65 is configured to determine the mass flow rate of the milled material based at least in part on the belt slippage of the conveyor belt 40 as determined by the belt slip determination circuit 66. For example, the belt slip determination circuit 66 is communicably coupled with the mass flow rate determination circuit 65 such that the mass flow rate determination circuit 65 can receive data, signals, or other indications from the belt slip determination circuit 66. The mass flow rate determination circuit 65 is configured to determine that the belt slippage (e.g., a belt slippage percentage) is beyond some threshold value and, based on that determination, calculate the mass flow rate of milled material using a first calculation method (e.g., a volume-based calculation method) instead of a second calculation method (e.g., a conveyor drive power-based calculation method) because the first calculation method is more accurate in instances where the conveyor belt 40 is slipping. Similarly, the mass flow rate determination circuit 65 is configured to determine that the belt slippage is within some range such that the second calculation method (e.g., the conveyor drive power-based calculation method) provides an accurate mass flow rate calculation.
Referring now to
At step 68, the method 67 includes receiving an indication of a speed of the motor 38. The speed of the motor 38 is a speed at which the motor 38 is configured to drive the conveyor belt 40 of the conveyor system 22. In some examples, the indication of the speed of the motor 38 can be a rotational speed of a component of the motor 38 (e.g., a drive wheel, a shaft, a gear, or some other component). In other examples, the indication of the speed of the motor 38 can be a hydraulic pressure of the motor 38 (e.g., where the motor 38 is a hydraulic motor) or an amperage of the motor 38 (e.g., where the motor 38 is an electric motor). The indication of the speed of the motor 38 can be provided to the controller 32 by the motor 38, by a speed sensor (e.g., an encoder) coupled with the motor 38, or by some other component or device operatively coupled with both the motor 38 and the controller 32.
At step 70, the method 67 includes receiving an indication of a speed of the conveyor belt 40. The speed of the conveyor belt 40 is a speed of the conveyor belt 40 that is driven by the motor 38 of the cold planer 10. The speed of the conveyor belt 40 is determined by the speed sensor 46. As discussed above, the speed sensor 46 can be a rotary encoder, a contactless encoder (e.g., an optical encoder, a magnetic encoder, a hall effect sensor), or some other speed sensor that is configured to determine a speed of the conveyor belt 40 or a speed of a roller 42 about which the conveyor belt 40 moves. For example, the speed sensor 46 can include the wheel 47 to ride along the roller 42 or the tight side of the conveyor belt 40 to obtain a measurement of the speed of the conveyor belt 40. The indication of the speed of the conveyor belt 40 can be provided to the controller 32 via the speed sensor 46 or by some other component or device that is operatively coupled with the speed sensor 46 and the controller 32.
At step 72, the method 67 includes determining an amount of belt slippage. The amount of belt slippage is an amount that the conveyor belt 40 is currently slipping relative to the speed of motor 38. As discussed above, the conveyor belt 40 can occasionally slip relative to the motor 38 such that the conveyor belt 40 moves at a speed that is less that the speed of the motor 38. The amount of belt slippage of the conveyor belt 40 is determined by the belt slip determination circuit 66 of the controller 32. In one example, the belt slip determination circuit 66 compares a speed of the motor 38, as represented by the received indication of the speed of the motor 38, with an actual speed of the conveyor belt 40, as represented by the received indication of the speed of the conveyor belt 40. A difference in speed between the conveyor belt 40 and the motor 38 is attributable to an amount of belt slippage.
At step 74, the method includes providing an indication of the amount of belt slippage. The controller 32 is communicably coupled with the operator interface 31 of the cold planer 10. For example, the indication of the amount of belt slippage can be a visual, audible, or other indication to the operator of the cold planer 10. The indication can indicate that the conveyor belt 40 is slipping by an amount more than some threshold amount. The indication can indicate that the conveyor belt 40 is slipping within some tolerance range. The indication can indicate that the conveyor belt 40 is not slipping or is slipping by an insubstantial amount. The controller 32 can provide periodic or continuous indications of the amount of belt slippage. For example, the belt slip determination circuit 66 can continuously determine the amount of belt slippage of the conveyor belt 40 and provide a real-time dynamic indication of a current amount of belt slippage. In some examples, the controller 32 is communicably coupled with the truck 34 or a remote monitoring device (e.g., a fleet management computing system). In such instances, the controller 32 can provide the indication of the amount of belt slippage to one or more devices other than the operator interface 31 of the cold planer 10.
At step 76, the method 67 includes determining a mass flow rate of milled material conveyed on the conveyor belt 40 of the cold planer 10. Specifically, the method 67 includes determining the mass flow rate of milled material based at least in part on the determined amount of belt slippage of the conveyor belt 40. As discussed above, depending on whether and the extent to which the conveyor belt 40 is slipping, the mass flow rate determination circuit 65 of the conveyor belt 40 is configured to calculate the mass flow rate of milled material using a particular calculation method to bolster the accuracy of the mass flow rate calculation. For example, a first mass flow rate calculation method (e.g., a force and belt speed-based calculation method) can be more accurate in circumstances where the conveyor belt 40 is not slipping or is only slipping by some amount less than a threshold amount. In such circumstances, the mass flow rate determination circuit 65 is configured to calculate the mass flow rate of the milled material according to the first mass flow rate calculation method. In other circumstances where the conveyor belt 40 is slipping beyond some threshold amount, the mass flow rate determination circuit 65 is configured to calculate the mass flow rate of milled material according to a second mass flow rate calculation method (e.g., a volume-based calculation method) that, in those circumstances, provides for a more accurate mass flow rate calculation. The mass flow rate determination circuit 65 is further configured to account for (e.g., adjust the calculation for) the determined amount of belt slippage to further bolster the accuracy of the mass flow rate calculation.
At step 78, the method 67 includes providing an indication of the determined mass flow rate of the milled material. Like step 74, the indication of the determined mass flow rate is provided to the operator interface 31 of the cold planer 10 as a visual, audible, or other indication. The indication can indicate an instantaneous, average, or target mass flow rate value, among other values. For example, the indication can indicate that the mass flow rate of milled material is within some tolerance range or outside of some tolerance range. The indication can indicate which mass flow rate calculation method is being used by the mass flow rate determination circuit 65 to calculate the mass flow rate. For example, the indication could visually depict to the operator that the mass flow rate determination circuit 65 of the controller 32 is using a belt scale calculation method to determine the mass flow rate of milled material. The mass flow rate determination circuit 65 can provide periodic or continuous indications of the mass flow rate of milled material. For example, the mass flow rate determination circuit 65 can continuously determine the mass flow rate of milled material on the conveyor belt 40 and provide a real-time dynamic indication of a current mass flow rate. In some examples, the controller 32 is communicably coupled with the truck 34 or a remote monitoring device (e.g., a fleet management computing system). In such instances, the controller 32 can provide the indication of the mass flow rate to one or more devices other than the operator interface 31 of the cold planer 10.
As described herein, the tension of the conveyor belt 40 has an effect on the mass flow rate calculation (e.g., as calculated by the mass flow rate determination circuit 65) and/or changes how the variables in the mass flow rate calculation are accounted for in the mass flow rate calculation. In general, the conveyor system 22 includes one or more tension screws that are configured to adjust the tension of the conveyor belt 40. For example, the tension screws may be manually or electronically (e.g., via an actuator) rotated, which results in increasing or decreasing (depending on the direction of rotation) the tension of the conveyor belt 40. Conventional tension screws do not include any active feedback or sensors that provide an indication of the actual tension of the conveyor belt, so initiating when to adjust the tension is a manual process that occurs at the discretion of an operator and the actual tension of the conveyor belt is not considered in conventional mass flow rate calculations.
Referring to
The tensioning assembly 100 includes a first tension screw 102 and a second tension screw 104. The first tension screw 102 and the second tension screw 104 are arranged on opposing sides of the drive roller 42A (e.g., axially-opposing sides) and are both configured to adjust a position of the drive roller 42A in response to rotation of the first tension screw 102 and the second tension screw 104. In some embodiments, the tensioning assembly 100 may include one tension screw (e.g., the first tension screw 102 or the second tension screw 104), rather than two tension screw arranged on opposing sides of the drive roller 42A. The first tension screw 102 is coupled to the drive roller 42A by a belt tensioner block 106, and the belt tensioner block 106 is coupled to the motor 38. In other words, the belt tensioner block 106 is coupled to the drive roller 42A, the motor 38, and the first tension screw 102. In some embodiments, the second tension screw 104 may be coupled to a belt tensioner block, similar to the belt tensioner block 106 but without being coupled to the motor 38 (i.e., the motor 38 is only coupled to the belt tensioner block 106 coupled to the first tension screw 102).
In general, the second tension screw 104 may include the similar components and functionality as the first tension screw 102 and, therefore, the following description of the first tension screw 102 applies to the second tension screw 104, with like elements identified using the same reference numerals. With continued reference to
With specific reference to
The adapter sleeve 120 includes a first sleeve 124 coupled between a first side of the tension sensor 122 and the first tension screw 102 and a second sleeve 126 coupled between a second side of the tension sensor 122 and the belt tensioner block 106. In some embodiments, the first sleeve 124 and the second sleeve 126 may be rigidly coupled to the tension sensor 122 so that the first sleeve 124 and the second sleeve 126 do not rotate relative to one another. Similarly, in some embodiments, the second sleeve 126 may be rigidly coupled to the belt tensioner block 106 so that the second sleeve 126 is fixed and does not rotate relative to the belt tensioner block 106. In some embodiments, the first sleeve 124 and the second sleeve 126 may be coupled to the tension sensor 122 via an adhesive, a fastener, a weld, or an equivalent coupling that prevents the first sleeve 124 from rotating relative to the second sleeve 126. In some embodiments, the second sleeve 126 may be coupled to the belt tensioner block 106 via an adhesive, a fastener, a weld, or an equivalent coupling that prevents the second sleeve 126 from rotating relative to the belt tensioner block 106.
The tension sensor 122 is mounted axially along the first tension screw 102 and configured to measure a tension on the conveyor belt 40. For example, the tension sensor 122 is axially aligned with the center axis 112 and is rigidly coupled to the first tension screw 102 so that the tension sensor 122 measures an axial force (e.g., compressive or tension) along the first tension screw 102 that is correlated to the tension of the conveyor belt 40. In some embodiments, the tension sensor 122 is a load cell (e.g., a hydraulic load cell, a pneumatic load cell, a strain gauge load cell, a piezoresistive load cell, an inductive load cell, etc.). Regardless of the particular implementation of the tension sensor 122, the tension sensor 122 is communicably coupled to the controller 32 and provides an output signal that is indicative of the tension of the conveyor belt 40 to the controller 32.
In some embodiments, during operation, one of the nuts (e.g., the first nut 114) is loosened from the frame plate 118, which allows the screw to translate or move in a direction along the central axis 112, and the other nut (e.g., the second nut 116) is rotated to move the first tension screw 102 in a direction along the central axis 112. In some embodiments, the first tension screw 102 may include threads that engage with the first nut 114 and the second nut 116. With the first tension screw 102 being rotationally fixed to the belt tensioner block 106, rotation of the first nut 114 or the second nut 116 (one of the nuts being loosened and the other being rotated) results in translation of the first tension screw 102 along the central axis 112 (the direction of translation being determined by the direction of rotation). Moving the first tension screw 102 along the central axis 112 results in the same movement of the belt tensioner block 106 and the motor 38 and the drive roller 42A coupled thereto. When the drive roller 42A is moved in a direction along the central axis 112 (e.g., parallel to a travel direction for milled material on the conveyor belt 40), the tension on the conveyor belt 40 is adjusted in accordance with the direction and magnitude of the movement of the drive roller 42A. This change in tension is captured by the tension sensor 122 by measuring the change in axial forces along the first tension screw 102 and output to the controller 32. Once the tension on the conveyor belt 40 is adjusted, the nut that was loosened (e.g., the first nut 114) may be tightened against the frame plate 118 to axially lock the first tension screw 102.
In some embodiments, during operation, both the first nut 114 and the second nut 116 are loosened and the belt tensioner block 106 is moved to adjust the tension on the conveyor belt 40. After the belt tensioner block 106 (and the motor 38 and the drive roller 42A coupled thereto) is moved, the first nut 114 and the second nut 116 may be retightened against the frame plate 118 resulting in a new axial force on the first tension screw 102 that is measured by the tension sensor 122 and communicated to the controller 32. In some embodiments, only the output from the tension sensor 122 of the first tension screw 102 is communicated to the controller 32. In some embodiments, the output from the tension sensor 122 of the first tension screw 102 and the output from the tension sensor 122 of the second tension screw 104 are both provided to the controller 32, and the controller 32 is configured to process the two outputs (e.g., average the two values, use the highest or lowest value, add the two values, etc.) to produce a single value representing the tension of the conveyor belt 40. In some embodiments, the tension sensors 122 of the first tension screw 102 and the second tension screw 104 may be used to accurately tension the conveyor belt 40 with the output values from the tension sensors 122 being displayed on the operator interface 31 (or another display). For example, the operator interface 31 may display a predefined tension value and a tolerance for the tension of the first tension screw 102, the second tension screw 104, and a total tension value. In some embodiments, the first tension screw 102 and the second tension screw 104 may be automatically adjusted (e.g., via a motor or an actuator) so that the tension sensors 122 may be used to actively adjust the tension of the conveyor belt 40.
The active measuring of the tension of the conveyor belt 40 enables the mass flow rate determination circuit 65 to more accurately calculate the mass flow rate of milled material and adjust the calculation and/or other variables accounted for in the calculation based on the tension measured by the tension sensor 122. With specific reference to
In some embodiments, the tension value of the conveyor belt 40 calculated by the belt tension determination circuit 130 may be utilized by the controller 32 in the direct calculation of the mass flow rate of milled material calculated by the mass flow rate determination circuit 65. For example, in some operating conditions, the mass flow rate determination circuit 65 is configured to calculate the mass flow rate of milled material using the conveyor drive power-based calculation method. The conveyor drive-power based calculation method utilizes the tension of the conveyor belt 40 as a variable in the calculation. As such, the empirical value for tension of the conveyor belt 40 calculated by the belt tension determination circuit 130, which is based on a direct measurement of the tension by the tension sensor(s) 122, is provided to the mass flow rate determination circuit 65, rather than using a theoretical value for belt tension that is not based on a measured value. This further bolsters the accuracy of the mass flow rate calculation that is calculated by the mass flow rate determination circuit 65.
In some embodiments, the tension value of the conveyor belt 40 calculated by the belt tension determination circuit 130 may be utilized by the controller 32 to adjust the mass flow rate of milled material calculated by the mass flow rate determination circuit 65. For example, the mass flow rate value calculated by the mass flow rate determination circuit 65 may be adjusted based on the tension value of the conveyor belt 40. In some embodiments, the calculated mass flow rate may be adjusted according to a linear correlation, a quadratic correlation, or based on an adjustment map that applies a correction factor to the calculated mass flow rate as a function of the tension value of the conveyor belt 40, which calculated by the belt tension determination circuit and measured by the tension sensor(s) 122. Accordingly, the active measurement and calculation of the tension of the conveyor belt 40 may be used to continuously adjust the calculated mass flow rate, and a change in the tension of the conveyor belt 40 may result in an accompanying adjustment of the calculated mass flow rate, which further bolsters the accuracy of the mass flow rate calculation that is calculated by the mass flow rate determination circuit 65.
In some embodiments, the tension value of the conveyor belt 40 calculated by the belt tension determination circuit 130 may be utilized by the controller 32 to adjust one or more variables used to calculate the mass flow rate of milled material calculated by the mass flow rate determination circuit 65. For example, a correction factor may be applied to one or more variables used in the calculation of the mass flow rate by the mass flow rate determination circuit 65. The correction factor may be correlated to the tension value based on a linear correlation, a quadratic correlation, or a correction map that applies a different correction factor to the one or more variables as a function of the tension value. For example, the correction factor may apply more or less weight to one or more variables in the calculation of the mass flow rate based on the tension of the conveyor belt 40. In some embodiments, the one or more variables include a hydraulic pressure associated with the motor 38, a hydraulic temperature, an angle of the conveyor belt 40, a speed of the conveyor belt 40, or a load acting on the conveyor belt 40 (e.g., as measured by the load cell(s) 45). Applying a correction factor, based on the tension of the conveyor belt 40, to one or more of the variables in the calculation of the mass flow rate further bolsters the accuracy of the mass flow rate calculation that is calculated by the mass flow rate determination circuit 65.
In some embodiments, the tension value of the conveyor belt 40 calculated by the belt tension determination circuit 130 may be utilized to select a mass flow rate determination method from a plurality of mass flow rate calculation methods (e.g., the volume-based calculation method, the conveyor drive power-based calculation method, and/or the force and belt speed-based calculation method). For example, a particular mass flow rate calculation method may be more accurate than another mass flow rate calculation method at different values for the tension of the conveyor belt 40, and the controller 32 may be configured to select a specific mass flow rate calculation method based on the tension of the conveyor belt 40. In this way, for example, the controller 32 is configured to further bolster the accuracy of the mass flow rate calculation that is calculated by the mass flow rate determination circuit 65.
In some embodiments, the controller 32 is configured to switch between the plurality of calculation methods based on the tension of the conveyor belt 40 exceeding or dropping below a threshold tension value. Alternatively or additionally, the controller 32 may be configured to calculate the mass flow rate using each of the plurality of calculation methods (e.g., the volume-based calculation method, the conveyor drive power-based calculation method, and/or the force and belt speed-based calculation method) and apply different weighting to each of the calculated mass flow rates (e.g., take a predetermined percentage of each calculated mass flow rate), based on the tension of the conveyor belt 40, to calculate a single value for the mass flow rate of milled material.
With reference to
At step 152, the method 150 measures the tension of the conveyor belt 40 via the tension sensor(s) 122 coupled to the first tension screw 102 and/or the second tension screw 104. In some embodiments, the tension measured at step 152 is calculated into a tension value by the belt tension determination circuit 130 as described herein. Once the tension of the conveyor belt 40 is measured at step 152, the mass flow rate of milled material is calculated at step 154. The mass flow rate of milled material is calculated by the mass flow rate determination circuit 65 according to the various methods described herein. For example, the tension of the conveyor belt 40 may be used in the conveyor drive-power based calculation method to calculate the mass flow rate at step 152. Alternatively, the volume-based calculation method or the force and belt speed-based calculation method may be implemented at step 152.
Once the mass flow rate is calculated at step 154, the controller 32 may detect, at step 156, a change in the tension of the conveyor belt 40 based on the measurement from the tension sensor(s) 122. In response to the change in the tension of the conveyor belt 40, the calculated mass flow rate is adjusted at step 158. The adjustment to the calculated mass flow rate may include one or more of applying a correction factor to the calculated mass flow rate based on the change in the tension of the conveyor belt 40, applying a correction factor to a variable used in the calculated mass flow rate based on the change in the tension of the conveyor belt 40, and/or changing the mass flow rate calculation method used to calculate the mass flow rate.
In some embodiments, the system 160 includes a mass flow system 165, a network 200, at least one sensor 205, a user device 210, and the cold planer 10. The system 160 and/or a component thereof includes at least one of the various systems, devices, and/or components described herein. For example, the mass flow system 165 includes the controller 32. In some embodiments, the system 160 and/or a component thereof perform similar functionality to one or more devices described herein. For example, the mass flow system 165 performs operations similar to the belt slip determination circuit 66. The sensor 205 may perform similar functionality to that of the belt tension sensors 122, for example. In some embodiments, system 160 and/or one or more components thereof may be distributed across and/or implemented via a server. For example, the mass flow system 165 may be housed and/or implemented by a server bank (e.g., one or more servers). Although
In some embodiments, the network 200 includes wired and/or wireless telecommunications. The network 200 includes at least one of the various networks described herein. For example, the network 200 may include Wide-Area Networks (WANs). As another example, the network 200 may include Local Area Networks (LANs). As another example, the network 200 may include a Controller Area Network (CAN). In some embodiments, the systems, devices, and/or components of the system 160 communicate via the network 200. For example, the sensors 205 may communicate with the cold planer 10 via the network 200.
In some embodiments, the sensors 205 include the angle sensors 41, the speed sensors 46, the load cell 45, and the belt tension sensors 122. In some embodiments, the sensors 205 include at least one of the various sensors and/or sensor types described herein. For example, the sensors 205 may include the angle sensor 41. As another example, the sensors 205 may include the speed sensor 46. In some embodiments, the sensors 205 may collect, measure, obtain, and/or otherwise acquire at least one of the various readings, measurements, and/or values described herein. For example, the sensors 205 may measure or calculate belt speed, the sensors 205 may measure or calculate an amount of force applied to the conveyor belt 40, or the sensors may measure or calculate some other parameter associated with the conveyor belt 40 or the cold planer 10.
In some embodiments, the user devices 210 includes at least one of the various display devices and/or computing devices described herein. For example, the user devices 210 includes the operator interface 31. In some embodiments, the user devices 210 includes at least one of a monitor, a display, a screen, a mobile device, a tablet, a laptop, a desktop, a computer, a computing device, and/or a handheld device.
In some embodiments, the mass flow system 165 includes at least one processing circuit 170 and at least one interface 195. In some embodiments, the processing circuit 170 includes at least one of the circuits and/or processing circuits described herein. In some embodiments, the processing circuits 170 includes at least one processor 175 and memory 180. Memory 180 includes one or more devices (e.g., Random Access Memory (RAM), Read Only Memory (ROM), Flash memory, hard disk storage) for storing data and/or computer code for completing and/or facilitating the various processes described herein. Memory 180 includes non-transient volatile memory, non-volatile memory, and non-transitory computer storage media. Memory 180 includes database components, object code components, script components, or any other type of information structure for supporting the various activities and information structures described herein. Memory 180 is communicably coupled to the processors 175 and memory 180 includes computer code or instructions (e.g., firmware or software) for executing one or more processes described herein.
The processors 175 are implemented as at least one of one or more application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), a group of processing components, or other suitable electronic processing components. Memory 180 stores one or more instructions that, when executed by the processors 175, cause the processors 175 to perform one or more of the various operations described herein. In some embodiments, memory 180 stores, keeps, and/or maintains at least one of records, tables, databases, data structures, and/or collections of information.
In some embodiments, the interface 195 includes at least one of network communication devices, network interfaces, and/or other possible communication interfaces. The interface 195 includes wired or wireless communications interfaces (e.g., jacks, antennas, transmitters, receivers, transceivers, wire terminals, etc.) for conducting data communications with various systems, devices, and/or components described herein. The interface 195 includes direct (e.g., local wired or wireless communications) and/or via a communications network (e.g., the network 200). For example, the interface 195 includes an Ethernet card and port for sending and receiving data via an Ethernet-based communications link or network. The interface 195 includes a Wi-Fi transceiver for communicating via a wireless communications network (e.g., the network 200). The interface 195 includes a power line communications interface. The interface 195 includes an Ethernet interface, a Universal Serial Bus (USB) interface, a serial communications interface, and/or a parallel communications interface. In some embodiments, the interface 195 connects and/or integrates the mass flow system 165 with the network 200.
In some embodiments, the memory 180 stores and/or includes at least one Machine Learning (ML) model 185 and a fusion module 190. The processing circuit 170 may access and/or use the ML model 185 to perform at least one of the various processes described herein. In some embodiments, the ML model 185 and/or the fusion module 190 are stored and/or located external to the mass flow system 165. For example, the ML model 185 and the fusion module 190 may be located in a remote database, and the mass flow system 165 communicates, via the network 200, with the remote database to access the ML model 185 and the fusion module 190. In some embodiments, the ML model 185 determines and/or predicts at least one of the various measurements and/or calculations described herein. For example, the ML model 185 may determine one or more mass flow rates for material of the conveyor belt 40. In some embodiments, the fusion module 190 may modify, adjust, aggregate, combine, and/or otherwise fuse one or more pieces of information together. For example, the fusion module 190 may fuse belt tension information with load cell information. In some embodiments, the fusion module 190 may be stored, in memory 180, as executable code that, when executed by the processors 175, cause the processors 175 to perform operations similar to that of the fusion module 190. In some embodiments, memory 180 may store and/or include at least one of the various circuits described herein. For example, memory 180 may include the belt slip determination circuit 66.
In some embodiments, the processing circuit 170 may detect one or more operations. For example, the processing circuit 170 may detect operations of the cold planer 10. In some embodiments, the processing circuit 170 may detect the operations based on one or more operational parameters. For example, the processing circuit 170 may detect that the motor 38 is operating and the processing circuit 170 may detect, based on operation of the motor 38, that the cold planer 10 is operating.
In some embodiments, the processing circuit 170 may detect movement of material on the conveyor belt 40. For example, the processing circuit 170 may receive, from the sensors 205, operational data that indicates movement of the conveyor belt 40. As another example, the processing circuit 170 may detect movement of material on the conveyor belt 40 using data provided by the sensors 205 that indicates a presence of material on the conveyor belt 40. In some embodiments, the processing circuit 170 may detect one or more of the various operations described herein.
In some embodiments, the processing circuit 170 may receive information corresponding to the cold planer 10. For example, the processing circuit 170 may receive, from the sensors 205, measurement data that corresponds to the cold planer 10. In some embodiments, the information may include at least one of the various types of measurement data described herein. For example, the processing circuit 170 may receive information that includes a speed of the conveyor belt 40. In some embodiments, the processing circuit 170 may receive the information from one or more sources (e.g., sensors). For example, the processing circuit 170 may receive information from a first sensor 205 and a second sensor 205. In some embodiments, the first sensor 205 may provide information that pertains to one or more first aspects of the cold planer 10. For example, the first sensor 205 may provide measurement data that is similar to that of the angle sensor 41. In some embodiments, the second sensor 205 may provide information that pertains to one or more second aspects of the cold planer 10. For example, the second sensor 205 may provide measurement data that is similar to that of the belt tension sensors 122.
In some embodiments, the processing circuit 170 may identify one or more aspects of the cold planer 10. For example, the processing circuit 170 may determine one or more aspects of the conveyor belt 40 using the information provided by the sensors 205. In some embodiments, the processing circuit 170 may identify at least one of the various aspects and/or statuses of the conveyor belt 40 described herein. For example, the processing circuit 170 may identify a belt tension for the conveyor belt 40 based on the tension sensor 122. In some embodiments, the processing circuit 170 may identify one or more aspects of one or more components and/or devices of the cold planer 10. For example, the processing circuit 170 may receive information, collected by the angle sensors 41, and the processing circuit 170 may determine, based on the information, an angle of the frame 11. The processing circuit 170 may receive information from some other sensor 205 or component of the cold planer 10 indicative of some other aspect, status, or operating condition of the cold planer 10 to identify an aspect of the cold planer 10. For example, the processing circuit may receive information from any number of sensors 205 or other sources, such as a GPS location of the cold planer 10, an identity of an operator of the cold planer 10, a time duration (e.g., a number of hours over the life of the cold planer 10 or a number of minutes of a particular operation), environmental conditions (e.g., current weather, humidity, forecasted weather, etc.) or some other parameter.
In some embodiments, the processing circuit 170 may identify the one or more aspects of the cold planer 10 by performing at least one of the various processes described herein. For example, a first aspect of the cold planer 10 may refer to belt slippage for the conveyor belt 40. To continue this example, the processing circuit 170 may determine (e.g., identify) the belt slippage for the conveyor belt 40 by performing operations similar to that of the belt slip determination circuit 66. In other examples, the processing circuit 170 may determine an amount of belt slippage of the conveyor belt 40 by otherwise receiving and/or performing operations with data associated with one or more of the motor 38, the angle sensor 41, the load cell 45, or the speed sensor 46.
In some embodiments, the processing circuit 170 may aggregate, accumulate, collect, and/or otherwise hold on to the information provided by the sensors 205. For example, the processing circuit 170 may store, in memory 180, the information provided by the sensors 205. In some embodiments, the fusion module 190 may receive the information that was provided by the sensors 205. The fusion module 190 may combine, aggregate, amalgamate, and/or otherwise fuse the information to create a fused set of information. For example, the fusion module 190 may receive, from a first sensor 205, a first set of information and the fusion module 190 may receive, from a second sensor 205, a second set of information. In some embodiments, the first set of information and the second set of information may correspond to different aspects of the cold planer 10. For example, the first set of information may correspond to belt slippage information and the second set of information may correspond to belt tension information. In other examples, the fusion module 190 receives a first set of information, a second set of information, and other sets of information that correspond to an amount of belt slippage, a tension on the conveyor belt, a speed of the motor 38 driving the conveyor belt 40, or some other parameter associated with the cold planer 10.
In some embodiments, the fusion module 190 may include, in the fused set of information, information that indicates the status of the cold planer 10. For example, the processing circuit 170 may determine, based on information provided by the sensors 205, that an amount of belt slippage for the conveyor belt 40 exceeds some threshold amount, which further indicates that the conveyor belt 40 is slipping by some undesirable or suboptimal amount. To continue this example, the fusion module 190 may include the data from sensors 205 used to determine that the conveyor belt 40 is slipping and/or an indication that the conveyor belt 40 is slipping in the fused set of information.
In some embodiments, the fusion module 190 may include, in the fused set of information, information that indicates measurements collected and/or obtained by the sensors 205. For example, the fusion module 190 may include, in the fused set of information, belt tension information. In some embodiments, the processing circuit 170 may perform, using the fused set of information, operations similar to the belt tension determination circuit 130 when determining belt tension of the conveyor belt 40.
In some embodiments, the processing circuit 170 may generate one or more mass flow rates. For example, the processing circuit 170 may determine a mass flow rate of material moving on the conveyor belt 40. In some embodiments, the processing circuit 170 may implement and/or use the ML model 185 to generate the mass flow rate of material moving on the conveyor belt 40. For example, the processing circuit 170 may provide and/or input the fused information set into the ML model 185 and the ML model 185 may provide one or more outputs (e.g., mass flow rates).
In some embodiments, the ML model 185 may be trained to correlate, detect, and/or determine given portions of the fused information, provided by the fusion module 190, to make predictions, estimations, or other determinations regarding the mass flow rate of material on the conveyor belt 40. For example, the ML model 185 may be trained to determine when to use belt slippage information as an input to generate a mass flow rate value. As another example, the ML model 185 may be trained to determine when to apply motor speed as an input to generate a mass flow rate value.
In some embodiments, the ML model 185 may be trained to implement and/or use one or more filters. For example, the ML model 185 may implement a Kalman filter, a particle filter, an extended Kalman filter, or some other linear or nonlinear estimation filter to generate one or more mass flow rates. In some embodiments, the ML model 185 uses the Kalman filter to generate predictions based on the fused set of information. For example, the ML model 185 uses the Kalman filter to generate mass flow rate predictions. To continue this example, the ML model 185 may continuously use the Kalman filter to update the mass flow predictions as subsequent information (e.g., subsequent fused sets of information) is received. In some embodiments, the ML model 185 may be trained to implement one or more techniques to perform parallel computations. For example, the ML model 185 may receive the fused set of information and the ML model 185 may generate one or more mass flow rates based on the fused set of information. To continue this example, the ML model 185 may select, from the mass flow rates, a given mass flow rate that matches and/or correlates to one or more inputs. Stated otherwise, the ML model 185 may generate mass flow rates based on the fused set of information and the ML model 185 may determine, from the mass flow rates, a given mass flow rate.
In some embodiments, the fused set of information may include information to use while performing at least one of the various calculations described herein. For example, the fused set of information may include information to perform the volume-based calculation described herein. As another example, the fused set of information may include information to perform the conveyor drive power-based calculation described herein. In some embodiments, the fused set of information may include information to perform one or more additional calculations. In some embodiments, the ML model 185 may generate the mass flow rate based on and/or using at least one of the various calculations described herein. For example, the ML model 185 may generate the mass flow rate using the force and belt speed-based calculation.
In some embodiments, the fused set of information may include equipment measurement data. For example, the fused set of information may include measurements collected by the speed sensor 46. To continue this example, the fused set of information may include a conveyor belt speed. As another example, the fused set of information may include inputs provided via the operator interface 31.
In some embodiments, the ML model 185 may compare the mass flow rate with one or more predetermined values. For example, the ML model 185 may compare the mass flow rate with one or more predetermined mass flow rates. To continue this example, the predetermined mass flow rates may refer to and/or include one or more rates of speeds. In some embodiments, the calculations used to determine, modify, and/or update the mass flow rate may depend on a given speed of flow (e.g., how fast and/or slow the material is moving). For example, when the flow of material is below a given threshold, a first set of inputs may be implemented by the ML model 185. As another example, when the mass flow rate is above a given threshold, a second set of inputs may be implemented by the ML model 185.
In some embodiments, the ML model 185 may compare the mass flow rate with the predetermined flow rates to make one or more adjustments. For example, the ML model 185 may continuously generate and/or update the mass flow rate and as the mass flow rate changes the ML model 185 may use different portions of the fused set of information. To continue this example, the ML model 185 may continuously select and/or choose given data to use when determining and/or updating the mass flow rate. In some embodiments, the ML model 185 may perform a first calculation using a first set of data when the previous mass flow rate was a first given value and perform a second calculation using a second set of data when the previous mass flow rate was a second given value.
In some embodiments, the processing circuit 170 may receive environmental data. For example, the sensors 205 may collect information to indicate an ambient temperature of an area including the cold planer 10. As another example, the sensors 205 may collect information to indicate a surface temperature of the conveyor belt 40. In some embodiments, the fusion module 190 may add and/or fuse the environmental data with the fused set of information. The ML model 185 may use the environmental data to perform one or more calculations. For example, the ML model 185 may determine, using the environmental data, one or more impacts on the mass flow rate. In some embodiments, the ambient temperature may impact and/or adjust flow rate of the conveyor belt and the ML model 185 may determine, based on the ambient temperature, the impact on the mass flow rate. The ML model 185 may update, revise, and/or modify the mass flow rate based on the impact attributed by the ambient temperature. In some embodiments, the ML model 185 may update the mass flow rate based on the environmental data.
In some embodiments, the processing circuit 170 may continuously and/or semi-continuously generate the mass flow rate. For example, the processing circuit 170 may be continuously updating and/or modifying the mass flow rate using subsequent information provided by the sensors 205. In some embodiments, the processing circuit 170 may determine, based on the mass flow rate, an amount of material deposited into a vehicle. For example, the processing circuit 170 may determine an amount of milled material that is deposited into the bed 36.
In some embodiments, the processing circuit 170 may determine and/or detect stoppage of the cold planer. For example, the processing circuit 170 may detect a stoppage of a flow of material on the conveyor belt 40. As another example, the processing circuit 170 may detect a selection of an icon displayed via the operator interface 31. To continue this example, the selection may indicate a stoppage of the flow of material on the conveyor belt 40. In other examples, the processing circuit 170 may detect stoppage of the cold planer 10 responsive to an operator of the cold planer 10 initiating and/or completing a shutdown sequence. The stoppage of the cold planer 10 may represent and/or indicate that no additional material is being deposited into the bed 36. In some embodiments, the ML model 185 may determine one or more amounts of material. For example, the ML model 185 may determine (e.g., predict and/or output) an amount of material deposited within the bed 36 based on the mass flow rate.
In some embodiments, the processing circuit 170 may provide one or more prompts. For example, the processing circuit 170 may prompt the user device 210 to provide information associated with the amount of material deposited within the bed 36. As another example, the processing circuit 170 may provide an indication of the determined amount of material deposited in the bed 36. To continue this example, the indication may include a prompt to confirm a measured amount of material in the bed 36.
In some embodiments, the processing circuit 170 may receive one or more responses. For example, the processing circuit 170 may receive responses from the user device 210. As another example, the processing circuit 170 may receive responses from the operator interface 31. In some embodiments, the responses may include an indication of a measure amount of material. For example, the indication may include a number and/or value that indicates an amount of material that was measured to have been in the bed 36.
In some embodiments, the processing circuit 170 may determine one or more differences. For example, the processing circuit 170 may determine differences between a determined (e.g., predicted) amount of material and a measured amount of material. Stated otherwise, the processing circuit 170 may determine differences between predictions (e.g., predicted amounts of material deposited in the bed 36) and the measured (e.g., actual amounts of material deposited in the bed 36, as determined by a truck scale or similar measurement device) amounts of material deposited into the bed 36. In some embodiments, the differences may include percentages, mathematical differences, ratios, and/or scales. For example, the ML model 185 may determine that X amount of material was deposited into the bed 36. To continue this example, the measured amount of material may be Y amount.
In some embodiments, the processing circuit 170 may train and/or retrain the ML model 185. For example, the processing circuit 170 may retrain the ML model 185 using the differences between the predicted amounts of material and the measured amounts of materials. In some embodiments, the processing circuit 170 may determine, based on the differences, that the ML model 185 is over-estimating and/or under-estimating an amount of material deposited in the bed 36. The processing circuit 170 may retrain the ML model 185 to adjust subsequent predictions of the ML model 185.
In some embodiments, step 220 may include receiving training data. For example, the processing circuit 170 may receive training data to use in training the ML model 185. In some embodiments, the training data can include sensor data (e.g., information and/or measurements collected) regarding operation of the cold planer 10. For example, the training data may include belt speed measurements, belt tension measurements, belt slippage measurements, among other training data. In some embodiments, the training data may also include information that indicates measured and/or determined amounts of material. For example, the sensor data may correspond to measurements taken as the cold planer 10 provided milled material to the bed 36 or after the cold planer 10 has provided milled material to the bed 36. The measured amounts of material may correspond to a weighed and/or measured amount of material located in the bed 36, which can be determined by a truck scale, for example.
In some embodiments, the training data may also include correlations between calculation techniques and cold planer information. For example, the training data may include a correlation between given mass flow rate calculations and statuses of the cold planer 10. Stated otherwise, the training data may indicate when given calculations may be used to determine mass flow rates. In particular, the training data may indicate when a particular mass flow rate calculation method (e.g., a volume-based calculation method, a conveyor drive power-based calculation method, a force and belt speed-based method, some combination thereof, or some other method).
In some embodiments, step 225 may include training the ML model 185. For example, the processing circuit 170 may input and/or provide the training data, received in step 220, to the ML model 185. In some embodiments, the processing circuit 170 may provide the training data in one or more increments and/or steps. For example, the processing circuit 170 may provide a first portion of the training data to use in training the ML model 185 and the processing circuit 170 may use a second portion of the training data to evaluate the ML model 185.
In some embodiments, the processing circuit 170 may evaluate the ML model 185 using one or more techniques. For example, the processing circuit 170 may evaluate the ML model 185 using a portion of the training data, which was not used to train the ML model 185. To continue this example, the processing circuit 170 may provide measurement data, included in the portion of the training data, to the ML model 185. Furthermore, the processing circuit 170 may evaluate the ML model 185 by comparing a prediction made by the ML model 185 with an actual measured amount that was included in the training data.
In some embodiments, step 230 may include receiving cold planer data. For example, the processing circuit 170 may receive measurement data from the sensors 205. In some embodiments, the processing circuit 170 may receive the measurement data responsive to operation of the cold planer 10. For example, the processing circuit 170 may receive the cold planer data from a cold planer 10 in operation. The cold planer data can be transmitted by the cold planer 10 responsive to the cold planer 10 material moving on the conveyor belt 40, responsive to an operator command, or otherwise.
In some embodiments, the processing circuit 170 may provide the cold planer data to the ML model 185. For example, the processing circuit 170 may input the data into the ML model 185. In some embodiments, the fusion module 190 may combine the cold planer data and the processing circuit 170 may provide the cold planer data as the fused set of information described herein. For example, the processing circuit 170 may receive a first portion of the cold planer data from a first sensor 205 and receive a second portion of the cold planer data from a second sensor 205. To continue this example, the fusion module 190 may combine the first portion of the cold planer data with the second portion of the cold planer data to create the fused set of information.
In some embodiments, step 235 may include generating a flow rate. For example, the ML model 185 may generate a mass flow rate using the cold planer data received in step 235. In some embodiments, the ML model 185 may generate the mass flow rate by evaluating the cold planer data and/or the fused set of information to identify one or more portions of the cold planer data. For example, the cold planer data may include information that indicates belt slippage and/or belt tension. To continue this example, the ML model 185 may determine to use a first portion of the cold planer data based on an amount of belt slippage (e.g., as determined by the belt slip determination circuit 66). As another example, the ML model 185 may determine to use a second portion of the cold planer data based on a belt tension (e.g., as determined by the belt tension determination circuit 130).
In some embodiments, the ML model 185 may generate the mass flow rate using at least one of the various calculations described herein. For example, the ML model 185 may generate the mass flow rate using the volume-based calculation. In some embodiments, the ML model 185 may select a given calculation based on the cold planer data. For example, the ML model 185 may perform a first calculation responsive to the cold planer data including a first set of information. As another example, the ML model 185 may perform a second calculation responsive to the cold planer data including a second set of information.
In some embodiments, step 240 may include receiving a measured amount of material. For example, the cold planer 10 may provide an amount of milled material to the bed 36. In some embodiments, ML model 185 may use the mass flow rate, generated in step 230, to determine an amount of milled material provided to the bed 36. When operation of the cold planer 10 stops and/or halts (e.g., no further milled material is provided), the amount of material deposited in the bed 36 may be measured. In some embodiments, the measured amount of material may include measuring a weight of the truck 34 before and after operation of the cold planer 10.
In some embodiments, step 245 may include determining a difference. For example, the ML model 185 may determine a difference between the measured amount of material, received in step 240, with a predicted amount of material. In some embodiments, the ML model 185 may determine the difference by comparing the measured amount of material with the predicted amount of material. For example, the measured amount of material may be X amount of material and the predicted amount of material may be Y amount. To continue this example, the difference may be a mathematical difference (e.g., X-Y and/or Y-X). In some embodiments, the difference may be a ratio and/or a percentage. For example, the difference may be that the measured amount of material 5% less than the predicted amount of material.
In some embodiments, step 250 may include determining if a threshold is exceeded. For example, a predetermined threshold may indicate and/or define a variance between the measured amount of material and the predicted amount of material. To continue this example, the predetermined threshold may be Z amount and/or Z percentage. In some embodiments, the process 215 may proceed to step 255 responsive to a determination that the difference, determined in step 245, does not exceed the threshold. In some embodiments, the process 215 may proceed to step 260 responsive to a determination that the difference, determined in step 245, exceeds the threshold. For example, the predetermined threshold may be Z amount and the ML model 185 may determine that the difference between the measured amount of the material and the predicted amount of material is 1.5Z or some other multiple or ratio.
In some embodiments, step 255 may include retraining the ML model 185. For example, the processing circuit 170 may use the difference, determined in step 245, to retrain the ML model 185. In some embodiments, the processing circuit 170 may retrain the ML model 185 by adjusting and/or modifying one or more weights of the ML model 185. For example, the ML model 185 may include a weight that corresponds to a given aspect of the cold planer 10. To continue this example, the processing circuit 170 may adjust the weight based on the difference. In some embodiments, the process 215 may proceed to step 230 responsive to the processing circuit 170 retraining the ML model 185.
In some embodiments, step 260 may include providing a validation. For example, the processing circuit 170 may display, via the interface 195, an indication that the difference, determined in step 245, did not exceed the threshold. In some embodiments, the processing circuit 170 providing the validation may indicate that the ML model 185 is generating predictions accurately and that the ML model 185 may be implemented in the field. For example, the process 215 may involve training the ML model 185 prior to the ML model 185 being implemented at a construction site and/or a construction project. In some embodiments, the processing circuit 170 providing the validation may indicate that initial training of the ML model 185 is complete and that subsequent usage of the ML model 185 may occur in the field.
In some embodiments, step 270 may include detecting an operation of the cold planer. For example, the processing circuit 170 may detect operation of the cold planer 10. In some embodiments, the processing circuit 170 may receive data from the sensors 205. For example, the processing circuit 170 may receive operational data from the sensors 205. In some embodiments, the operational data may include runtime information. For example, the operational data may include information indicating that the motor 38 is on and/or operating. As another example, the processing circuit 170 may detect a selection of an icon on the operator interface 31.
In some embodiments, step 275 may include receiving information that correspond to the cold planer. For example, the processing circuit 170 may receive information from the sensors 205. In some embodiments, the information may include at least one of the various types of measurement data described herein. For example, the processing circuit 170 may receive information that indicates an amount of force on the conveyor belt 40. As another example, the processing circuit 170 may receive information that indicates a belt tension.
In some embodiments, step 280 may include identifying a first status of the cold planer. For example, the processing circuit 170 may determine a first status of the conveyor belt 40 using the information received in step 275. In some embodiments, the first status of the conveyor belt 40 may include at least one of the various statuses of the conveyor belt 40 described herein. For example, the first status may include a belt slippage of the conveyor belt 40. As another example, the first status may include a belt tension of the conveyor belt 40.
In some embodiments, the processing circuit 170 may perform at least one of the processes described herein to identify the first status. For example, the first status may include a belt slippage and the processing circuit 170 may perform operations similar to the belt slip determination circuit 66 to identify the belt slippage. In some embodiments, the first status of the cold planer may include an angle of the frame 11 and the processing circuit 170 may use the information, received in step 275, to determine a measured angle of the frame 11.
In some embodiments, step 285 may include identifying a second status of the cold planer. For example, the processing circuit 170 may determine a second status of the conveyor belt 40. In some embodiments, the processing circuit 170 may use the information, received in step 275, to identify the second status. For example, the second status may include a belt tension and the information, received in step 275, may be similar to information provided by the belt tension sensors 122.
In some embodiments, the processing circuit 170 may perform at least one of the processes described herein to identify the second status. For example, the second status may include a belt tension and the processing circuit 170 may perform operations similar to the belt tension determination circuit 130 to identify the belt tension. In some embodiments, the second status of the cold planer 10 may include a speed of the motor 38 and the processing circuit may determine, using the information received in step 275, to determine the speed of the motor 38.
In some embodiments, step 290 may include combining the information to create a fused set of information. For example, processing circuit 170 may receive, in step 275, information from a first sensor 205 and information from a second sensor 205. In some embodiments, the fusion module 190 may combine the information provided by the first sensor 205 and the information provided by the second sensor 205 to create the fused set of information.
In some embodiments, the fused set of information may be provided to the ML model 185. For example, the fusion module 190 may input the fused set of information to the ML model 185. In some embodiments, the fused set of information may include information that otherwise may not be accessible to the ML model 185. For example, the processing circuit 170 may receive, in step 275, a first set of information that pertains to motor speed and a second set of information that pertains to belt tension. To continue this example, if the first set of information and the second set of information were provided to the ML model 185 separately, the ML model 185 may not have the complete picture.
By providing the fused set of information to the ML model 185, the ML model 185 can have the complete picture. For example, by providing motor speed information and belt tension information together, the ML model 185 can determine which portions of the information to use when making predictions.
In some embodiments, step 295 may include generating a mass flow rate of material on the conveyor belt. For example, the ML model 185 may generate a mass flow rate of material on the conveyor belt 40. In some embodiments, the ML model 185 may generate the mass flow rate of material using the fused set of information created in step 290. For example, the fused set of information may include at least one of the various types of measurement data described herein and the ML model 185 may generate, using the fused set of information, the mass flow rate based on and/or using at least one of the various calculations described herein.
The disclosed solutions contain several industrial applications. In general, the controller 32 of the cold planer 10 includes the belt slip determination circuit 66 to determine when and the extent to which the conveyor belt 40 of the conveyor system 22 is slipping. The controller 32 further includes the mass flow rate determination circuit 65 that is configured to calculate the mass flow rate of milled material on the conveyor belt 40 based on an indication from the belt slip determination circuit 66. The mass flow rate determination circuit 65 is configured to implement a specific mass flow rate calculation method (e.g., one of a volume-based calculation method, a conveyor drive power-based calculation method, or a force and belt speed-based method) based at least in part on information regarding slippage of the conveyor belt 40 as determined by the belt slip determination circuit 66. Specifically, the mass flow rate determination circuit 65 is configured to implement a specific mass flow rate calculation method based on an amount of belt slippage experienced by the conveyor belt 40 and as measured by the belt slip determination circuit 66. Likewise, the mass flow rate determination circuit 65 is configured to account for an amount of belt slippage experienced by the conveyor belt 40 in the mass flow rate calculation.
The disclosed solutions provide for more accurate mass flow rate calculations, which in turn provide an operator (e.g., an operator of the cold planer 10 or an operator of the truck 34) with greater confidence regarding a weight of milled material deposited in the bed 36 of the truck 34. In particular, because the controller 32 includes the mass flow rate determination circuit 65 configured to calculate mass flow rate of the milled material based at least in part on an amount of belt slippage of the conveyor belt 40, the mass flow rate determination circuit 65 can avoid using inaccurate mass flow rate calculation methods when the conveyor belt 40 is slipping an undesirable amount, for example. If the conveyor belt 40 is experiencing slippage beyond some threshold amount, the mass flow rate determination circuit 65 can calculate the mass flow rate of milled material using a first mass flow rate calculation method that is most accurate given the belt slippage experienced by the conveyor belt. If, however, the conveyor belt 40 is experiencing no belt slippage or an amount of belt slippage within some tolerance range, the mass flow rate determination circuit 65 can use a second mass flow rate calculation method that is even more accurate than the first mass flow rate calculation method in such circumstances. Moreover, the mass flow rate determination circuit 65 can account for (e.g., factor in) the belt slippage of the conveyor belt 40 as measured by the belt slip determination circuit 66 to further bolster accuracy of mass flow rate calculations.
The controller 32 is further configured to provide an indication to the operator of the cold planer 10 or some other operator (e.g., an operator of a remote monitoring service, an operator of the truck 34, or otherwise) to alert the operator of various parameters or conditions, which provides the operator with greater control of the cold planer 10 and related operations. Specifically, the controller 32 can provide an indication to the operator interface 31 of the cold planer 10 as to the mass flow rate (e.g., an instantaneous mass flow rate, an average mass flow rate, or some other value), an amount of belt slippage of the conveyor belt 40, a weight of milled material transferred to the truck 34, or some other value. Such indications can allow the operator to quickly identify issues (e.g., a worn conveyor belt 40) and rectify them in a timely manner. In addition, real-time reliable indications regarding mass flow rate and a weight of milled material transferred to the bed 36 of the truck 34 can provide for more efficiency roadway milling operations by allowing the bed 36 of the truck 34 to be filled substantially to capacity (e.g., greater than 90% capacity) without risk of inadvertently over-filling the truck and potentially incurring financial penalties as a result.
The disclosed solutions further provide for more accurate mass flow rate calculations by actively measuring and accounting for the tension of the conveyor belt 40 using the tension sensor(s) 122, which in turn provide an operator (e.g., an operator of the cold planer 10 or an operator of the truck 34) with greater confidence regarding a weight of milled material deposited in the bed 36 of the truck 34. In particular, because the controller 32 includes the belt tension determination circuit 130 that provides the mass flow rate determination circuit 65 with an indication of a tension of the conveyor belt 40 that is actively measured by the tension sensor(s) 122, the mass flow rate determination circuit 65 can more accurately calculate the mass flow rate and account for variations in the tension of the conveyor belt 40. The mass flow rate determination circuit 65 can also adjust the calculation of the mass flow rate of milled material based on the tension of the conveyor belt 40 by applying a correction factor to the calculated mass flow rate based on the change in the tension of the conveyor belt 40, applying a correction factor to a variable used in the calculated mass flow rate based on the change in the tension of the conveyor belt 40, and/or changing the mass flow rate calculation method used to calculate the mass flow rate, all of which further bolster accuracy of mass flow rate calculations.
The disclosed solutions further provide improved accuracy in mass flow rate measurements. In particular, the utilization of Machine Learning models (e.g., the ML model 185) along with the fusion module 190 allows the mass flow system 165 to generate predictions (e.g., mass flow rate measurements) that otherwise may be without given sensor data. The fusion module 190 combining (e.g., fusing) the information collected by the sensors 205 and providing the fused set of information to the ML model 185 allows the ML model 185 to implement, given the fused set of information, an applicable mass flow calculation to a given circumstance. If the ML model 185 was given only a portion of the sensor information, the ML model 185 may be unable to generate accurate predictions. However, the fusion module 190 inputs the fused set of information to allow in the ML model 185 to continuously select which calculation to implement when determining and/or updating mass flow rate calculations. Moreover, the ML model 185 accounts for variance and/or impacts on the mass flow rate calculations based on portions of the fused set of information that identifies multiple aspects or statuses of the cold planer 10 (e.g., belt slippage, belt tension, ambient temperature, motor speed, force applied to conveyor belt, etc.).
As utilized herein with respect to numerical ranges, the terms “approximately,” “about,” “substantially,” and similar terms generally mean+/−10% of the disclosed values, unless specified otherwise. As utilized herein with respect to structural features (e.g., to describe shape, size, orientation, direction, relative position, etc.), the terms “approximately,” “about,” “substantially,” and similar terms are meant to cover minor variations in structure that may result from, for example, the manufacturing or assembly process and are intended to have a broad meaning in harmony with the common and accepted usage by those of ordinary skill in the art to which the subject matter of this disclosure pertains. Accordingly, these terms should be interpreted as indicating that insubstantial or inconsequential modifications or alterations of the subject matter described and claimed are considered to be within the scope of the disclosure as recited in the appended claims.
It should be noted that the term “exemplary” and variations thereof, as used herein to describe various embodiments, are intended to indicate that such embodiments are possible examples, representations, or illustrations of possible embodiments (and such terms are not intended to connote that such embodiments are necessarily extraordinary or superlative examples).
The term “coupled” and variations thereof, as used herein, means the joining of two members directly or indirectly to one another. Such joining may be stationary (e.g., permanent or fixed) or moveable (e.g., removable or releasable). Such joining may be achieved with the two members coupled directly to each other, with the two members coupled to each other using a separate intervening member and any additional intermediate members coupled with one another, or with the two members coupled to each other using an intervening member that is integrally formed as a single unitary body with one of the two members. If “coupled” or variations thereof are modified by an additional term (e.g., directly coupled), the generic definition of “coupled” provided above is modified by the plain language meaning of the additional term (e.g., “directly coupled” means the joining of two members without any separate intervening member), resulting in a narrower definition than the generic definition of “coupled” provided above. Such coupling may be mechanical, electrical, or fluidic.
References herein to the positions of elements (e.g., “top,” “bottom,” “above,” “below”) are merely used to describe the orientation of various elements in the figures. It should be noted that the orientation of various elements may differ according to other exemplary embodiments, and that such variations are intended to be encompassed by the present disclosure.
It is important to note that the construction and arrangement of the cold planer 10 and the components thereof as shown in the various exemplary embodiments is illustrative only. Additionally, any element disclosed in one embodiment may be incorporated or utilized with any other embodiment disclosed herein.
