Patent Application
20250197174
This disclosure pertains to the field of hoisting appliances such as cranes, gantry cranes or overhead travelling cranes. This disclosure notably relates to a method for optimizing operation of such hoisting appliances to allow reliable predictive maintenance.
Hoisting appliances 1 such as bridge cranes, gantry cranes or overhead travelling cranes usually comprise a trolley 2 which can move over a single girder or a set of rails 3 along a horizontal axis Y, as shown in
A tool 4, also called load suspension device, is associated with a reeving system having cables which pass through the trolley 2, the length of the cables 5 being controlled by the trolley 2 to vary, thereby enabling displacement of a load 6 along a vertical axis Z, referred to as hoisting movement.
In addition, some hoisting appliances such as cranes may have additional movable parts, allowing an angular movement of the load, such as a rotation around the vertical Z axis.
Such hoisting appliances, when used in an industrial environment or in ports and harbors, operate at high dynamics and speeds and are part of critical processes which are subject to high production requirements. There is therefore a strong need of predictive maintenance on such hoisting appliances, which allows detecting failure before it happens and informing the operator of the hoisting appliances accordingly.
Indeed, repairing or replacing a defective product always presents a bigger cost, as compared to a planned intervention:
Until now, some suspicious phenomena could be detected directly by an operator during the normal operation of the hoisting appliance, offering the opportunity to a maintenance team to investigate a yet to come failure. But with the increasing use of autonomous systems, these earlier detections have disappeared, and have given rise to a need for a solution that can determine with a high level of confidence the future failure of a component before it happens.
To answer this need, one technological approach used in industry is to implement a solution based on digital twin, which reproduces the normal behavior of all—or part—of a system. Such a solution relies on development of one or several virtual sensor(s), which can efficiently predict physical values, such as torque, vibration, or temperature, that depend on the health of a machine.
The aim of a virtual sensor is to predict what would be a normal value at a precise operating point of the system and compare it with a real measured value. A significant difference between the measured value and the predicted value is then a sign of abnormal operation, and a hint that a failure may be about to occur.
The creation of a virtual sensor often implies a teaching phase, to learn how the system behaves. It is mandatory to collect and record the relevant data that directly influence the considered physical value. For example, the temperature of a motor depends on the electrical current, the speed, and the ambient temperature. If some of these data are missing, it is difficult—or impossible—to create a relevant virtual sensor, which would accurately predict the temperature of a motor. Collecting enough relevant data to feed the model on which the virtual sensor relies during this learning phase is therefore crucial.
However, one of the most common problems encountered during a learning phase is the difficulty (or even impossibility) of browsing all the machine's operating points.
Indeed, if all the relevant data are available, it is then necessary to carry out a significant number of recordings, to ensure that all the system's operating points are covered. For example, for a motor temperature, recordings should be made over the entire ambient temperature range (e.g. from 0° C. to 40° C.) to which the motor will be subjected. If the learning phase does not consider the entire ambient temperature range, the result of the virtual sensor in these missing learning areas will be false or at least inaccurate.
Similarly, if the volume of relevant data is very big, it becomes clear that learning cannot cover all operating points. For example, referring to the example of
This leads to a very high computational cost, coupled to a heavy burden on the operator of the crane for browsing the entire range of possible values for all eight parameters. In practice, such an exhaustive learning phase cannot be carried out.
A practical solution to reduce the number of operating points would be reducing the number of relevant data, by limiting motions of the hoisting appliance. In the previous example of a vibration virtual sensor on a crane's hoist motion in the steel industry, it could be possible, to allow efficient predictive maintenance, to forbid all other motions that could interfere in the vibration of the hoist motor. In such a way, it would be possible to develop a vibration virtual sensor which would only take into account the operating parameters of the hoist motor, and which would provide accurate predictions on its state of health, as long as its operation is isolated from the other motions of the crane, which would be forbidden.
A drawback of this solution is that forbidding combined motion of the different movable parts of the hoisting appliance decreases the performance of the crane. Such a decrease in productivity, which would directly result from an additional constraint implied by the predictive maintenance function, would hamper development of fully automated hoisting systems capable of transferring suspended loads autonomously along a trajectory.
Accordingly, there is a need for a method for operating a hoisting appliance providing high performance of production and efficient predictive maintenance result.
This disclosure improves the situation.
It is proposed a method for operating a hoisting appliance spanning a hoisting area, the hoisting appliance comprising N≥2 movable parts for the transport of a load from a starting point to a destination point, the N movable parts being configured for a linear movement along any of three X, Y and Z orthogonal axes or for an angular movement, the method comprising, in a control device, choosing speed parameters for displacement of the N movable parts for transporting the load from the starting point to the destination point, by:
In another aspect, it is proposed an apparatus for operating a hoisting appliance spanning a hoisting area, the hoisting appliance comprising N≥2 movable parts for the transport of a load from a starting point to a destination point, the N movable parts being configured for a linear movement along any of three X, Y and Z orthogonal axes or for an angular movement, the apparatus comprising:
In another aspect, it is proposed a computer software comprising instructions to implement at least a part of a method as defined here when the software is executed by a processor. In another aspect, it is proposed a computer-readable non-transient recording medium on which a software is registered to implement the method as defined here when the software is executed by a processor.
The following features, can be optionally implemented, separately or in combination one with the others:
The method further comprises a preliminary learning phase to create a digital twin model of the hosting appliance which is fed by a point cloud collected during normal operation of the hosting appliance, the point cloud comprising operating points defined as N-uplets of values for each of the speed parameters of the N movable parts, and the operating zone is defined as a zone for which a density of the point cloud is greater than a determined threshold.
Determining a set of speed parameters for displacement of said N movable parts comprises:
Selecting, among said set, speed parameters which minimize a travel time of said load comprises:
Assigning a high priority level to one of the movable parts comprises:
Selecting the speed parameters among the set is only performed if they allow achieving a productivity of the hoisting appliance greater than a determined threshold of productivity.
The hoisting appliance comprises a gantry and a trolley able to transport the load suspended to a hoist mechanism hosted in the trolley, the gantry is able to move substantially horizontally along the X axis, the trolley is able to move substantially horizontally along the Y axis, the hoist mechanism is able to move substantially vertically along the Z axis and the hoisting appliance comprises a rotation tool able to move angularly.
Other features, details and advantages will be shown in the following detailed description and on the figures, on which:
The figures and the following description illustrate specific exemplary embodiments of the disclosure. It will thus be appreciated that those skilled in the art will be able to devise various arrangements that, although not explicitly described or shown herein, embody the principles of the disclosure and are included within its scope. Furthermore, any examples described herein are intended to aid in understanding the principles of the disclosure and are to be construed as being without limitation to such specifically recited examples and conditions. As a result, the disclosure is not limited to the specific embodiments or examples described below, but by the claims and their equivalents.
It is now referred to
A hoisting area, such as a warehouse, a yard, a hall or other working area, is provided with a supervisory system SUP that is an IT control system for supervision of the hoisting area. The supervisory system provides information to the control device CD for trajectory execution, authorization i.e. access management, and security in general.
The control device CD is able to communicate with the supervisory system SUP, with the set of meter devices MD and with the digital twin DT through a telecommunication network TN. The telecommunication network may be a wired or wireless network, or a combination of wired and wireless networks. The telecommunication network can be associated with a packet network, for example, an IP (“Internet Protocol”) high-speed network such as the Internet or an intranet or even a company-specific private network. The control device CD may be Programmable Logic Controllers (PLC) and other automation device able to implement industrial processes and able to communicate with the supervisory system for exchanging data such as requests, inputs, control data, etc.
In one embodiment, the set of meter devices MD includes devices for metering operating parameters of the hoisting appliance, such as a vibration sensor of the hoist motor, a torque meter, a temperature sensor, etc.
In one embodiment, the digital twin DT includes virtual sensors for predicting physical values of operating parameters of the hoisting appliance. Preferably, each virtual sensor of the digital twin DT corresponds to a meter device MD, thus allowing the control device CD to compare digital twin DT outputs to the observed physical values output by the set of metering devices MD.
The control device CD is configured to create a path to be followed by the crane for transporting a load from one place within the hoisting area to another.
The control device CD is configured to choose speed parameters for any movable part of the hoisting appliance which fulfill a joint objective of minimizing the travel time along the created path and operating the hoisting appliance in an operating zone for which the predictive maintenance function provided by the digital twin DT yields results which are above a determined accuracy threshold. For example, the control device CD chooses the values of the trolley speed, the gantry speed, the hoist speed and the rotation speed in the hoisting appliance and makes sure that these values allow operating the hoisting appliance in an operating zone for which the predictive maintenance function relying on the digital twin DT is reliable, while maximizing the production performance of the hoisting appliance.
With reference to
In step S1, the control device CD determines operating zones of the hoisting appliance for which the predictive maintenance function relying on the digital twin DT provides reliable results. Such operating zones are areas comprising enough known operating points of the hoisting appliance, which have been browsed during a learning phase used to create the digital twin DT. Operating the hoisting appliance in these operating zones assures to obtain an efficient predictive maintenance result.
In step S2, given a target request for transporting a load along a path from a starting point to a destination point in the hoisting area, the control device CD determines a set of speed parameters of the movable parts of the crane (e.g., trolley speed, gantry speed, hoist speed, rotation speed) which allow answering the request and which belong to the operating zones determined in step S1.
In step S3, the control device CD selects, within this set of speed parameters, the speed parameters of the movable parts of the crane which minimize the travel time of the load along the path, and hence, maximize the production performance of the crane.
Such a method may be integrated into a method for performing predictive maintenance of the hoisting appliance during its operation, as illustrated by the flow chart of
Initially, in step S01, the control device CD performs a learning phase, during which operating points of the hoisting appliance are browsed during normal operation of the crane: relevant data is collected by the set of metering devices MD and recorded. In an embodiment, the collected data matches a point cloud comprising operating points defined as N-uplets of values for each of the speed parameters of the N movable parts of the hoisting appliance (for example, N=4 and the N-uplet of values comprises trolley speed, gantry speed, hoist speed and rotation speed). Such a learning phase may take place during a span of time ranging from several days to several weeks of normal operation of the hoisting appliance.
In step S02, a digital twin model is created to reproduce the normal behavior of all or part of the hoisting appliance. For example, the digital twin model comprises a virtual vibration sensor for the hoist motor, a virtual temperature sensor, a virtual torque sensor for predicting vibration, temperature and torque values at a precise operating point of the hoisting appliance (e.g. for a given speed of the trolley, the gantry, the hoist and the rotation tool). Such a digital twin model relies on a mathematical model of the hoisting appliance and of the interactions between its movable parts and is known in the prior art. The digital twin DT may be stored by the control device CD, or in a remote computing device in communication with the control device through the telecommunication network TN. Operations performed in steps S01 and S02 allow creation of the predictive maintenance function of the hoisting appliance.
Once steps S01 and S02 are completed, predictive maintenance of the hoisting appliance may be achieved while it is operated by the control device CD.
As described previously in relation to
For example, the area can be gridded (e.g. with squares of 10% by 10%, i.e. 100 squares). When there is a sufficient number of points in a square (e.g. 3), the square is included in the “known area”. The determined threshold for the density of points may be set to 3 points/% of the surface in an embodiment.
In step S2, the control device CD analyzes the path to be travelled by the load, from a starting point to a destination point, and assigns priority levels to the different motions in the hoisting appliance. For example, taking account of the maximum possible speed of every movable part in the crane, the motion with the maximum travel time from the starting point to the destination point is assigned the highest priority level, as it is the motion which limits the production performance of the crane. This analysis of the path allows determining a set of speed parameters which both allow meeting the target request assigned to the hoisting appliance and belong to the operating zones.
In step S3, and as will be described in greater detail below in relation to
In step S4, the set of metering devices MD measures data during crane movement along the path of the load. Step S5 is an end of movement test which leads to either continuing data measurement of step S4 if the hoisting appliance is still in movement for transport of the load to its destination point, or feeding the digital twin DT with the measured data in step S6 if the load has reached its destination point.
In step S7, the control device CD compares the physical values predicted by the digital twin DT to the observed physical values measured by the set of metering devices MD. If a significant difference between the predicted and observed values is assessed in step S8, it may be a hint for an abnormal phenomenon, or a future defect or breakdown of a part. The control device CD raises an alert at step S9, which may take the form of an alert message sent to the supervisory system SUP. The alert message may be displayed on a Graphical User Interface GUI to inform an operator of the hoisting appliance that a maintenance intervention must be planned.
After step S9, or directly after step S8 in case the digital twin outputs match the physical values observed by the set of metering devices MD, the control device CD is ready to perform a new target request at step S10, and to create a new path to be followed by the crane for transporting a load from a new starting point to a new destination point within the hoisting area.
Operations performed by the control device CD during steps S1 to S3 will now be described in greater details in relation to
To reach this objective, the control device must, on the one hand, analyze the path corresponding to the target of the hoisting appliance to maximize production level (steps S2 and S3) and, on the other hand, take account of the operating zones (step S1) in which monitoring the hoisting appliance for predictive maintenance provides reliable results.
In an exemplary embodiment, the hoisting appliance receives a request for transporting a load in the hoisting area from a starting point to a destination point. The control device CD first calculates the distance to be travelled by each movable part of the crane to answer this request. The table below provides an example of distance to be travelled by each movable part, along with its maximum possible speed.
The data in Table 1 allow computing the optimal operating time for achieving each requested motion, as indicated in the last column of Table 2 below.
As may be observed, the most constraining time is associated to gantry motion, as it takes 25 s for the gantry to travel from its starting point to its destination point, and it will hence be the movable part which completes its motion last. In this exemplary embodiment, the gantry motion is hence assigned the highest priority level and is chosen as the reference motion. Gantry is the reference movable part.
This 25 s travel time induces the maximum production performance level of the hoisting appliance, which may be noted as t_prod_max. It allows computing, for each other movable part (trolley, hoist, rotation) a minimum speed below which the maximum production performance level, t_prod_max, would be degraded. For each movable part, the minimum speed is calculated as follows: min_speed=distance/t_prod_max. Table 3 provides the minimum speed computed for each movable part.
It is then possible to compute, for each movable part, a ratio of this minimum speed to their maximum possible speed: Ratio=min_speed/max_speed. Results are recorded in Table 4 below.
The computed ratios allow deriving a speed synchronizing curve for each movable part (trolley, hoist, rotation) as a function of the speed of the reference movable part, i.e. of the gantry speed, such that all four motions (along the X, Y, Z axes and the angular movement) terminate at the same time. These speed synchronizing curves are illustrated in dashed lines in
As may be understood when looking at
In other words, if, for any reason, the gantry speed should be reduced to, say, 50% of its maximum speed, it would be possible to reduce the speed of the three other movable parts in the same ratio, without decreasing the production performance of the crane more severely: the travel time of the hoisting appliance would then be 50 s.
Points 51 to 53 on
Moreover, these speed parameters must also be chosen so as to achieve a reliable predictive maintenance for the hoisting appliance.
Operating zones of the hoisting appliance in which the predictive maintenance function yields results above a determined accuracy or reliability threshold must be determined (step S1).
When observing the 2D representation of the operating points cloud on
The speed synchronizing curves of
In this exemplary embodiment, the set of remarkable operating points for the trolley corresponds to a range of trolley speed values comprised between 0 and 90% of the gantry speed, as shown on
The control device CD determines the optimal solution for achieving the target request by selecting the best percentage of gantry speed for which a corresponding speed for the other movable parts of the hoisting appliance belongs to the set of remarkable operating points shown as dots on
In this exemplary embodiment, the optimum time for achieving the target request by the hoisting appliance, while allowing reliable monitoring of the crane is 33.3 s. The production performance level of the hoisting appliance is hence decreased by 33%, as compared to the maximum production level t_prod_max which would have been achieved with a travel time of 25 s. Yet, it allows a reliable prediction of the state of health of the hoisting appliance which, in the long term, guarantees a satisfying production performance level, by avoiding unplanned interruptions to machine operation.
It is however possible, in an embodiment, to set a maximum threshold of production level decrease, below which the control device CD cannot go. This threshold can be adapted e.g., by an operator of the crane, depending on the production constraints of the hoisting appliance and may vary over time or cycles of operation. For example, the operator may decide that the production performance level should not be smaller than 60% of the maximum production level t_prod_max. In the above exemplary embodiment, it means the maximum time for achieving the request by the hoisting appliance should not be greater than 35 s.
In case the control device CD cannot determine speed parameters for the gantry, trolley, hoist mechanism and rotation tool which satisfy this constraint, it may decide to run the hoisting appliance at its maximum speed and waives its monitoring through use of the digital twin. In such a case, it may emit an alert, which can be sent to the supervisory system SUP. The alert message may be displayed on a Graphical User Interface GUI to inform an operator of the hoisting appliance that the predictive maintenance function is deactivated.
An embodiment comprises a control device CD under the form of an apparatus comprising one or more processor(s) I/O interface(s), and a memory coupled to the processor(s). The processor(s) may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. The processor(s) can be a single processing unit or a number of units, all of which could also include multiple computing units. Among other capabilities, the processor(s) are configured to fetch and execute computer-readable instructions stored in the memory.
The functions realized by the processor may be provided through the use of dedicated hardware as well as hardware capable of executing software in association with appropriate software. When provided by a processor, the functions may be provided by a single dedicated processor, by a single shared processor, or by a plurality of individual processors, some of which may be shared. Moreover, explicit use of the term “processor” should not be construed to refer exclusively to hardware capable of executing software, and may implicitly include, without limitation, digital signal processor (DSP) hardware, network processor, application specific integrated circuit (ASIC), field programmable gate array (FPGA), read only memory (ROM) for storing software, random access memory (RAM), and non volatile storage. Other hardware, conventional and/or custom, may also be included.
The memory may include any computer-readable medium known in the art including, for example, volatile memory, such as static random access memory (SRAM) and dynamic random access memory (DRAM), and/or non-volatile memory, such as read-only memory (ROM, erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes. The memory includes modules and data. The modules include routines, programs, objects, components, data structures, etc., which perform particular tasks or implement particular abstract data types. The data, amongst other things, serves as a repository for storing data processed, received, and generated by one or more of the modules.
A person skilled in the art will readily recognize that steps of the methods, presented above, can be performed by programmed computers. Herein, some embodiments are also intended to cover program storage devices, for example, digital data storage media, which are machine or computer readable and encode machine-executable or computer-executable programs of instructions, where said instructions perform some or all of the steps of the described method. The program storage devices may be, for example, digital memories, magnetic storage media, such as a magnetic disks and magnetic tapes, hard drives, or optically readable digital data storage media.
| Number | Date | Country | Kind |
|---|---|---|---|
| 23307263.6 | Dec 2023 | EP | regional |
