Patent Application
20230058093
The subject matter described herein relates to control systems that monitor and control movement of vehicles, and may detect and account for unstable movement conditions of the vehicles (e.g., instability conditions, derailment conditions, and the like).
Discussion of Art.
Instability of vehicles may be caused by various events or factors, such as damage to mechanical components of a multi-vehicle group or multi-vehicle system (e.g., wheels, suspension, axles, and/or bearings), damage to mechanical components of a route (e.g., increasing distances between inner sides of heads of rails of a track, inclinations or breakages of the rail(s), or other changes to the surface of a route that may or may not include a track or rails), structural geometric defects of wheels or the route, and/or unpredictable external causes, such as the presence of foreign objects on the route. Accidents may be a frequent events that can have serious consequences and may present complex phenomena. These accidents may include derailments as one example.
Some systems for detecting accidents such as derailments allow risks of derailment to be simultaneously analyzed based on a few parameters (e.g., one or, at most, two parameters). For example, WILD sensors (wheel impact load detectors) may include strain gauges or load cells that allow vertical loads transferred from vehicle wheels to a route surface to be measured. The presence of defects on the wheel (e.g., insufficiently circular shapes, flat surfaces, or other tread defects) can generate abnormal loads on the route. These abnormal or unexpected loads may initiate an accident such as derailment, and may be evaluated. Some other known systems may measure lateral forces and provide a yaw index of axles of the vehicles. These systems may concurrently measure vertical forces to determine a risk of an accident (e.g., derailment) due to flange hopping or other faults with the wheels. Some other systems may be limited to trying to limit consequences of an accident such as a derailment, rather than prevent or limit the conditions leading to the accident. Such systems may activate only an emergency braking system after the accident occurs. These systems, however, may not be able to predict the accident and therefore may be unable to prevent the accident. For example, KR20120014092 describes a system based on sensors that measure the distance between the rail and the vehicle. In this system, a signal is provided only when one or more wheels have left their normal operating position. Which is to say, when the derailment has already happened.
In some other known systems, to detect accident conditions, each vehicle may be equipped with a monitoring device capable of detecting accelerometer and gyroscope signals. Comparison rules, algorithms, and tables can be established for the signals to detect when the travel conditions turn from a normal travel condition to an abnormal condition, which may coincide with an accident such as a derailment.
For safety reasons, it may be necessary to ensure that such systems have redundant structures that allow the system to function properly even if one of the electronic components malfunctions. For example, in EP2165912, a device for monitoring the instability of a railway vehicle is described. The device includes a first accelerometer providing an acceleration signal in response to vibrations along a reference axis, a solid-state relay switchable between a closed condition and an open condition, and a programmable logic device (e.g., a field programmable gate array (FPGA)) connected to the accelerometer and to the solid-state relay. The programmable logic device includes non-volatile logic blocks for the simultaneous parallel execution of an instability monitoring algorithm to change a state of the relay based on a condition of instability of the acceleration signal. The use of a single FPGA component may not guarantee the necessary hardware and software redundancy of the system. Even if non-volatile logic blocks are used for simultaneous parallel execution of a monitoring algorithm, in the presence of damage to the FPGA, the detection system may not be able to perform its task properly.
It may be desirable to have a system and method that differs from those that are currently available.
In one example, a vehicle control system is provided and may include a sensor that may measure an acceleration and a displacement of a vehicle during movement of the vehicle. The system may include one or more processors that can examine the acceleration and the displacement to identify an abnormal condition of the vehicle that includes an instability condition and/or a derailment condition of the vehicle. The processors may identify the instability condition responsive to the acceleration that is measured being no greater than an acceleration threshold and the displacement that is measured being no greater than a displacement threshold. The processors may identify the derailment condition responsive to the acceleration that is measured being greater than the acceleration threshold and the displacement that is measured being greater than the displacement threshold.
In another example, a method for controlling a vehicle may include measuring an acceleration of a vehicle during movement of the vehicle using a sensor, measuring a displacement of the vehicle during the movement of the vehicle using the sensor, and identifying an instability condition and/or a derailment condition of the vehicle using one or more processors. The instability condition may be identified responsive to the acceleration that is measured being no greater than an acceleration threshold and the displacement that is measured being no greater than a displacement threshold. The derailment condition may be identified responsive to the acceleration that is measured being greater than the acceleration threshold and the displacement that is measured being greater than the displacement threshold.
In another example, a control system may include a sensor means that may measure an acceleration, a linear displacement, and an angular displacement of a vehicle during movement of the vehicle. The control system also may include one or more processors that may examine the acceleration, the linear displacement, and the angular displacement to identify one or more of an instability condition or a derailment condition of the vehicle. The processors may identify the instability condition responsive to the acceleration that is measured being no greater than an acceleration threshold, the linear displacement that is measured being no greater than a linear displacement threshold, and the angular displacement that is measured being no greater than an angular displacement threshold. The one or more processors may identify the derailment condition responsive to the acceleration that is measured being greater than the acceleration threshold, the linear displacement that is measured being greater than the linear displacement threshold, and the angular displacement that is measured being greater than the angular displacement threshold.
The subject matter may be understood from reading the following description of non-limiting embodiments, with reference to the attached drawings, wherein below:
A control system for detecting an instability condition of a vehicle 1 may include a first control chain C1 and a second control chain C2. The control chains C1 and C2 may be connected to a digital output 0 to which a load L is connected. The control chains can represent hardware circuitry that includes and/or is connected with one or more processors (e.g., one or more FPGAs, integrated circuits, microprocessors, and the like) that perform the operations described herein in connection with the control system and/or the control chains. The digital output may represent one or more conductive pathways and/or wireless pathway along or through which signals are communicated or conveyed, such as cables, wires, buses, inductive connections, wireless connections, and the like
The digital output 0 may supply or not supply electric power needed to activate the load L. The power may be supplied or not supplied to the load L according to detection of an abnormal instability condition of the vehicle, such as an abnormal gait. In the configurations shown in
The first control chain may include a first sensor means (e.g., a first sensor) S1 that can detect first detection data of an attitude and/or movement of the vehicle. The first control chain may include a first control unit U1 that may receive the first detection data of the attitude and/or movement of the vehicle from the first sensor. The first control unit may represent one or more processors, and can execute a first computer program SW1 that checks the first detection data for evidence of an abnormal instability condition of the vehicle. The first control unit may control a first electronic control means or device E1 to supply or not to supply power to the load L. The first electronic control device can represent one or more mechanical or electronic switches that control conduction of electric current to the load under the direction of the first control unit.
The second control chain C2 may include a second sensor means or sensor S2 that may detect second detection data of the attitude and/or movement of the vehicle. The second control chain may include a second control unit U2 that may receive the second detection data from the second sensor. This second control chain may include one or more of the same processors as the first control chain and/or may include one or more additional or other processors that are not included in the first control chain. The second control chain may execute a second computer program SW2 for checking an abnormal instability condition. The second control chain may control a second electronic control means or device E2 to supply or not to supply power to the load L.
Each of the first and second sensor means may include both at least one accelerometric sensor and at least one gyroscopic sensor. The second electronic control device can represent one or more mechanical or electronic switches that control conduction of electric current to the load under the direction of the second control unit. The first and second electronic control devices may include one or more of the same switches, or may include different switches.
The second sensor may be different from the first sensor means. The second control unit may be different from the first control unit. The second computer program may be different from the first computer program. The second electronic control device may be different from the first electronic control device. For example, each of the sensors, devices, programs, or units may be different from each other in that the first sensor/unit/program/device may not be the exact same hardware and/or software as the second sensor/unit/program/device.
The first computer program may use the first detection data received by the first sensor and the second computer program may use the second detection data received from the second sensor to detect the abnormal instability condition. Stated differently, each of the first and second control units may receive the first detection data, the second detection data, or both the first and second detection data.
With reference to
The first and second electronic control devices E1, E2 may be in a normally open condition or state. This condition or state may change to a closed condition or state responsive to the power being supplied to the system by detection of the abnormal instability condition of the vehicle.
In this way, if even one of the first and second electronic control devices, or a component of the first or second control chains does not work properly due to failure, damage, or the like, the control system of the abnormal instability condition of the vehicle may enter into a safety condition where an alarm can be generated. Moreover, if only one of the two control chains detects the abnormal condition of the vehicle, this may be sufficient to stop supply of power to the load through the first or second electronic control devices. This can allow for increased or a maximum probability of intervention in the event of the abnormal instability condition of the vehicle.
By way of example, the load connected to the digital output may transfer the detection of the abnormal condition of the vehicle to a detection and signaling device located inside a cabin of the vehicle. This signaling device may warn an operator of the vehicle RV visually and/or or acoustically of the occurrence of the abnormal condition of the vehicle.
The load L may also be or include a control system capable of acting directly on a brake system of the vehicle. For example, the load L may be a discharge valve connected to a brake pipe of a railway vehicle. Such valve may be included in the load L that is controlled by the control system for detecting the abnormal condition. The valve may be used to control pressure relief in the brake pipe. For example, controlled sequences of changes (e.g., reductions and/or increases) in pressure in the brake pipe may be generated by opening and closing of the valve. These pressure changes can represent information and can be decoded by the signaling device in an operator's cab of the vehicle. The detection and signaling device located inside the operator's cab may include or represent one or more input and/or output devices, such as display devices, speakers, touchscreens, and the like The detection and device may apply emergency braking by continuously or repeatedly powering the load L in response to detecting the abnormal condition of the vehicle. As another example, the detection and signaling device may present a request that an engine driver reduce speed by activating the load L with a predefined discharge sequence. A discharge sequence may be a pattern or order of pressure discharges in the brake system, with the discharges separated in time. One example of such a sequence may include two discharges lasting three seconds each and separated from each other by a two second pause of no discharges. If the detection and signaling device does not detect a speed decrease of the vehicle within a predetermined period of time from the beginning or end of this sequence, then the detection and signaling device, or at least one of the control devices, may automatically initiate emergency braking of the brake system automatically. As another example, the detection and signaling device may request the driver or operator of the vehicle to stop the vehicle as soon as possible with, for example, a discharge sequence in response to detecting the abnormal condition of the vehicle. This discharge sequence may include three pressure discharges lasting three seconds each and separated from each other by a two second pause. If the detection and signaling device does not detect stopping or slowing of the vehicle within a predetermined period of time, emergency braking may start automatically.
The first and second electronic control devices may be in a normally open condition and may be brought into a closed condition responsive to power being supplied to the control system. Therefore, if a single fault concerns a component of only one of the first and second control chains of the control system, the alarm may not be generated. This can reduce instances of false positives or false detections of instability. For example, the preceding description for other examples where only a single control chain needs to detect instability, another embodiment may require that both or multiple (where there are more than two) of the control chains detect the abnormal condition of the vehicle to stop powering the load L through the first or second electronic control devices (and thereby send an alarm signal). This may make it possible to increase availability of the vehicle by limiting false detections.
The first control unit and the second control unit may each include a different or separate microprocessor from the other. This may mean that different components may not have only two distinct elements, but also may have different structural features.
For example, the different microprocessors may be or may include microprocessors having internal structures that are different from each other and/or that use different technologies. The computer programs may have different codes. The likelihood of an occurrence of a hardware or software malfunction of both control units may be reduced relative to the units having the same design (due to the same design and/or manufacturing defect).
The first electronic control unit may include at least one relay device R and the second electronic control unit may include at least one metal-oxide-semiconductor (MOS) device M, or vice versa, to provide more independent hardware redundancy between the control chains. A second relay device R2 may be provided to avoid or reduce overheating of the relay device R due to a prolonged operating period in which the relay device R is supplied with power. These relay devices R, R2 may be used alternately to reduce the overheating. For example, the relay device R may be used first, then the second relay device R2, then the relay device R, and so on. Alternatively, only one of the relay devices R, R2 may be used. Other types of electronic control units may also be used, for example safety relays, acoustic devices (e.g., sirens), communication networks, and the like
The control system may detect and distinguish between a derailment condition and an instability condition of the vehicle, and generate respective alarm signals based on which condition is detected. For example, in the event of detection of a derailment condition (e.g., the vehicle leaving the route, such as by the wheels of the vehicle no longer being on the rails, the road, or the like), the load L may be continuously or repeatedly de-energized and, in the event of detection of an instability condition (e.g., the vehicle laterally swaying, undulating, oscillating, and the like, above the route), the load L may be de-energized in a non-continuous or irregular way (e.g., not at a consistent frequency), with a predetermined signaling frequency. Alternatively, the control system for detecting the derailment 1 may further comprise a CAN network for sending or receiving system signals for detecting the instability condition, and, in the event of detection of a derailment condition, the load L may be de-energized continuously or repeatedly. A respective instability alarm signal may be sent via the CAN network for signaling to an operator, regardless of the load L.
As illustrated in
The control system may be installed on a frame of a truck 20 of the vehicle or in another location of the vehicle. The enclosure may include a cover 20that can be removed to access the circuitry of the control system for maintenance, installation, or inspection purposes. To eliminate or reduce high frequency components that may interfere with the sensors, the control system may include at least one low-pass filter coupled with the sensors. This filter may remove frequencies from signals having frequencies in excess of a frequency threshold of the filer. The enclosure may contain a resin 84 in which the at least one electronic board is immersed. This resin may operate or act as the low-pass filter within the control system. The electronic board may not be fixed to the enclosure in any way in one embodiment. This can avoid transmitting vibrations to which the enclosure is subjected with a frequency higher than those of interest to the electronic board. For example, the board may be separated from and spaced apart from the enclosure by the resin, air, and/or other materials so that vibrations of the enclosure are not sensed by the board and/or do not change or negatively impact the values acquired by the first and second sensors. A dedicated frame may be used to hold the electronic board in position during casting of the resin. The resin may be cast into the enclosure until the electronic board is covered to the desired level. The frame may be removed after the resin has hardened or cured, and a floating encapsulation is thus obtained (e.g., the board is in the resin but does not directly contact the enclosure due to the resin being between the board and the enclosure). The electronic board therefore may not be rigidly fixed to any mechanical part, such as the enclosure. The resin may be selected for the appropriate hardness for the application to dampen the frequencies not of interest and avoid low frequency resonances. The floating encapsulation may provide shock and vibration protection. For example, the resin may mechanically isolate the electronic board from the mechanical part (e.g., the enclosure) on which the board will be installed, and may act as a damper for vibrations and shocks.
Being able to filter high frequency vibrations, the resin may allow the use of less expensive accelerometers, such as MEMS accelerometric sensors. Less expensive accelerometers may not allow filters to be added to limit the band between the sensing elements of the accelerometers and the analog digital conversion parts of the accelerometers. In the absence of resin, the use of such components may be limited to environments that are not subject to vibrations in relatively high frequencies (e.g., frequencies greater than the threshold frequency of the low pass filter) due to problems of aliasing and saturation of the sensing elements.
The resin may allow for lower sampling frequencies of the sensors to be used. The frequency may be at least twice the maximum frequency of the measured signal (e.g., the measured vibrations), or another value. The first sensor and/or the second sensor may comprise at least one MEMS accelerometric sensor.
The concepts described herein concerning the resin and the floating encapsulation may be applied to a variety of detection systems and not only to the control system for detecting an abnormal instability condition of a vehicle having the features described above.
A method for detecting an abnormal condition of a vehicle also is provided. This abnormal condition can be a derailment condition and/or an instability condition as described above. The method may include a step of acquiring first detection data of the attitude and/or movement of the vehicle using the first sensor of the first control chain, a step of transmitting the first detection data from the first sensor to the first control unit of the first control chain, a step of executing the first computer program for checking an abnormal condition of the vehicle using the first control unit of the first control chain, and a step of controlling, by the first control unit, the first electronic control device of the first control chain as a function of the decisions of the first computer program for checking the abnormal condition of the vehicle. This controlling may involve supplying or cutting off power to the load.
The method also may include a step of acquiring second detection data of the attitude and/or movement of the vehicle by the second sensor of the second control chain that is different from the first sensor, a step of transmitting the second detection data from the second sensor to the second control unit of the second control chain that is different from the first control unit, a step of executing the second computer program for checking the abnormal condition of the vehicle that is different from the first computer program for checking the abnormal condition of the vehicle, and a step of controlling, by the second control unit, the second electronic control device of the second control chain that is different from the first electronic control device according to the decisions of the second computer program for checking the abnormal condition of the vehicle. This controller may involve supplying or cutting off power to the load.
As shown in
By way of example, a method for detecting an abnormal condition of a vehicle may be provided. Detection of the abnormal condition of the vehicle may base detection of the abnormal movement condition of a truck of a vehicle, such as a railway vehicle, on one or more criteria. These criteria may include an acceleration level, a linear displacement, and/or an angular displacement. If a measured acceleration exceeds one or more upper acceleration thresholds or levels, the measured acceleration may indicate or identify a possible impact of the truck with ballast. This can be a consequence of a possible derailment. Lower values of the measured acceleration (e.g., values that are lower than one or more lower acceleration thresholds or levels) may identify or indicate possible damage to the mechanics of the truck, wheels, bearings, and the like of the vehicle.
With respect to linear displacement, the route or rails may not allow high transient linear displacements more than a designated linear threshold displacement. A movement or sudden movement of more than this threshold displacement (e.g., more than a few centimeters on any axis) may indicate a possible derailment. Similarly, frequent presence of transient movements of less than this threshold (e.g., a few centimeters or less on the longitudinal and transverse axes) may indicate an instability of the vehicle. The frequent presence may occur when the transient movements are detected at least as often as a designated frequency.
With respect to angular displacement, similar to the linear displacement, the route or rails may not allow high transient angular displacements. A movement or sudden movement of more than a threshold angular displacement (e.g., more than a few degrees, such as more than three or five degrees) on one or more (or any) axis may indicate a possible derailment. Periodic or other transient movements of less than this threshold angular displacement on the longitudinal and/or transverse axes, on the other hand, may indicate an instability of the vehicle.
Detection of the derailment and/or instability may combine the criteria set out above. Each criterion may contribute with different weights to a detection. For example, if a predetermined threshold is exceeded, a derailment alarm signal is generated. If, on the other hand, a second predetermined lower threshold than the first threshold is surpassed, an instability signal may be generated. For example, in the frequency domain, comparison frequency masks may be used to detect abnormal conditions. This may make it possible to obtain a detection having a greater accuracy than systems that use only vertical acceleration data. These systems may be affected by a problem of generating false signaling when calibrated too sensitively, or of losing a correct signal when calibrated in a less sensitive way.
While one or more embodiments are described in connection with a rail vehicle system, not all embodiments are limited to rail vehicle systems. Unless expressly disclaimed or stated otherwise, the subject matter described herein extends to other types of vehicle systems, such as automobiles, trucks (with or without trailers), buses, marine vessels, aircraft, mining vehicles, agricultural vehicles, or other off-highway vehicles. The vehicle systems described herein (rail vehicle systems or other vehicle systems that do not travel on rails or tracks) may be formed from a single vehicle or multiple vehicles. With respect to multi-vehicle systems, the vehicles may be mechanically coupled with each other (e.g., by couplers) or logically coupled but not mechanically coupled. For example, vehicles may be logically but not mechanically coupled when the separate vehicles communicate with each other to coordinate movements of the vehicles with each other so that the vehicles travel together (e.g., as a convoy).
In one embodiment, the control system may have a local data collection system deployed that may use machine learning to enable derivation-based learning outcomes. The controller may learn from and make decisions on a set of data (including data provided by the various sensors), by making data-driven predictions and adapting according to the set of data. In embodiments, machine learning may involve performing a plurality of machine learning tasks by machine learning systems, such as supervised learning, unsupervised learning, and reinforcement learning. Supervised learning may include presenting a set of example inputs and desired outputs to the machine learning systems. Unsupervised learning may include the learning algorithm structuring its input by methods such as pattern detection and/or feature learning. Reinforcement learning may include the machine learning systems performing in a dynamic environment and then providing feedback about correct and incorrect decisions. In examples, machine learning may include a plurality of other tasks based on an output of the machine learning system. In examples, the tasks may be machine learning problems such as classification, regression, clustering, density estimation, dimensionality reduction, anomaly detection, and the like. In examples, machine learning may include a plurality of mathematical and statistical techniques. In examples, the many types of machine learning algorithms may include decision tree based learning, association rule learning, deep learning, artificial neural networks, genetic learning algorithms, inductive logic programming, support vector machines (SVMs), Bayesian network, reinforcement learning, representation learning, rule-based machine learning, sparse dictionary learning, similarity and metric learning, learning classifier systems (LCS), logistic regression, random forest, K-Means, gradient boost, K-nearest neighbors (KNN), a priori algorithms, and the like. In embodiments, certain machine learning algorithms may be used (e.g., for solving both constrained and unconstrained optimization problems that may be based on natural selection). In an example, the algorithm may be used to address problems of mixed integer programming, where some components restricted to being integer-valued. Algorithms and machine learning techniques and systems may be used in computational intelligence systems, computer vision, Natural Language Processing (NLP), recommender systems, reinforcement learning, building graphical models, and the like. In an example, machine learning may be used for vehicle performance and behavior analytics, and the like.
In one embodiment, the control system may include a policy engine that may apply one or more policies. These policies may be based at least in part on characteristics of a given item of equipment or environment. With respect to control policies, a neural network can receive input of a number of environmental and task-related parameters. These input parameters may include one or more of the acceleration levels, linear displacements, and/or angular displacements. The neural network can be trained to generate an output based on the inputs, with the output representing an identification of an unstable or instability of the vehicle, a derailment of the vehicle, or no instability or derailment of the vehicle. The output optionally can include an action or sequence of actions that the vehicle or vehicle group should take, such as to slow or stop movement responsive to identifying instability, calling emergency personnel to the location of the vehicle responsive to identifying derailment, or the like. During operation of one embodiment, a determination can occur by processing the inputs through the parameters of the neural network to generate a value at the output node designating that action as the desired action. This action may translate into a signal that causes the vehicle to operate (e.g., automatically). This may be accomplished via back-propagation, feed forward processes, closed loop feedback, or open loop feedback. Alternatively, rather than using backpropagation, the machine learning system of the controller may use evolution strategies techniques to tune various parameters of the artificial neural network. The controller may use neural network architectures with functions that may not always be solvable using backpropagation, for example functions that are non-convex. In one embodiment, the neural network has a set of parameters representing weights of its node connections. A number of copies of this network are generated and then different adjustments to the parameters are made, and simulations are done. Once the output from the various models are obtained, they may be evaluated on their performance using a determined success metric. The best model is selected, and the vehicle controller executes that plan to achieve the desired input data to mirror the predicted best outcome scenario. Additionally, the success metric may be a combination of the optimized outcomes, which may be weighed relative to each other.
The controller can use this artificial intelligence or machine learning to receive input (e.g., acceleration levels, linear displacements, and/or angular displacements), use a model that associates the inputs with different conditions (e.g., instability, derailment, no instability, no derailment, and the like) to select a condition, and then provide an output (e.g., a signal that automatically slows or stops the vehicle, a signal that is automatically sent to emergency personnel, and the like, using the model). The controller may receive additional input of the output that was selected, operator input, or the like, that indicates whether the machine-selected output provided a desirable outcome or not. Based on this additional input, the controller can change the model, such as by changing which output would be selected when a similar or identical input is received the next time or iteration. The controller can then use the changed or updated model again to select an output, receive feedback on the selected output, change or update the model again, and the like, in additional iterations to repeatedly improve or change the model using artificial intelligence or machine learning.
In one example, a vehicle control system is provided and may include a sensor that may measure an acceleration and a displacement of a vehicle during movement of the vehicle. The system may include one or more processors that can examine the acceleration and the displacement to identify an abnormal condition of the vehicle that includes an instability condition and/or a derailment condition of the vehicle. The processors may identify the instability condition responsive to the acceleration that is measured being no greater than an acceleration threshold and the displacement that is measured being no greater than a displacement threshold. The processors may identify the derailment condition responsive to the acceleration that is measured being greater than the acceleration threshold and the displacement that is measured being greater than the displacement threshold.
The processors may change movement of the vehicle responsive to identifying the instability condition. The sensor may measure the displacement as a linear displacement of the vehicle. The sensor may measure the displacement as an angular displacement of the vehicle. The displacement may be a linear displacement, the displacement threshold may be a linear displacement threshold, and the sensor may measure an angular displacement of the vehicle. The processors may identify the instability condition responsive to the acceleration that is measured being no greater than the acceleration threshold, the linear displacement that is measured being no greater than the displacement threshold, and the angular displacement being no greater than an angular displacement threshold. The processors may identify the derailment condition responsive to the acceleration that is measured being greater than the acceleration threshold, the linear displacement that is measured being greater than the displacement threshold, and the angular displacement being greater than the angular displacement threshold.
The system also may include an enclosure in which the sensor and the processors are disposed. The sensor and the processors may be separated from the enclosure by a low pass filter. The low pass filter may be a resin disposed between the enclosure and each of the sensor and the one or more processors.
In another example, a method for controlling a vehicle may include measuring an acceleration of a vehicle during movement of the vehicle using a sensor, measuring a displacement of the vehicle during the movement of the vehicle using the sensor, and identifying an instability condition and/or a derailment condition of the vehicle using one or more processors. The instability condition may be identified responsive to the acceleration that is measured being no greater than an acceleration threshold and the displacement that is measured being no greater than a displacement threshold. The derailment condition may be identified responsive to the acceleration that is measured being greater than the acceleration threshold and the displacement that is measured being greater than the displacement threshold.
The method also may include changing movement of the vehicle responsive to identifying the instability condition. The displacement may be measured by the sensor as a linear displacement of the vehicle. The displacement may be measured by the sensor as an angular displacement of the vehicle. The displacement may be measured by the sensor as a linear displacement, the displacement threshold may be a linear displacement threshold, and the method may also include measuring an angular displacement of the vehicle using the sensor.
The instability condition may be identified responsive to the acceleration that is measured being no greater than the acceleration threshold, the linear displacement that is measured being no greater than the displacement threshold, and the angular displacement being no greater than an angular displacement threshold. The derailment condition may be identified responsive to the acceleration that is measured being greater than the acceleration threshold, the linear displacement that is measured being greater than the displacement threshold, and the angular displacement being greater than the angular displacement threshold.
In another example, a control system may include a sensor means that may measure an acceleration, a linear displacement, and an angular displacement of a vehicle during movement of the vehicle. The control system also may include one or more processors that may examine the acceleration, the linear displacement, and the angular displacement to identify one or more of an instability condition or a derailment condition of the vehicle. The processors may identify the instability condition responsive to the acceleration that is measured being no greater than an acceleration threshold, the linear displacement that is measured being no greater than a linear displacement threshold, and the angular displacement that is measured being no greater than an angular displacement threshold. The one or more processors may identify the derailment condition responsive to the acceleration that is measured being greater than the acceleration threshold, the linear displacement that is measured being greater than the linear displacement threshold, and the angular displacement that is measured being greater than the angular displacement threshold.
The processors may change movement of the vehicle responsive to identifying the instability condition. The system also may include an enclosure in which the sensor means and the processors are disposed. The sensor means and the processors may be separated from the enclosure by a low pass filter. The low pass filter may be a resin disposed between the enclosure and each of the sensor means and the one or more processors.
Use of phrases such as “one or more of . . . and,” “one or more of . . . or,” “at least one of . . . and,” and “at least one of . . . or” are meant to encompass including only a single one of the items used in connection with the phrase, at least one of each one of the items used in connection with the phrase, or multiple ones of any or each of the items used in connection with the phrase. For example, “one or more of A, B, and C,” “one or more of A, B, or C,” “at least one of A, B, and C,” and “at least one of A, B, or C” each can mean (1) at least one A, (2) at least one B, (3) at least one C, (4) at least one A and at least one B, (5) at least one A, at least one B, and at least one C, (6) at least one B and at least one C, or (7) at least one A and at least one C.
As used herein, an element or step recited in the singular and preceded with the word “a” or “an” do not exclude the plural of said elements or operations, unless such exclusion is explicitly stated. Furthermore, references to “one embodiment” of the invention do not exclude the existence of additional embodiments that incorporate the recited features. Moreover, unless explicitly stated to the contrary, embodiments “comprising,” “comprises,” “including,” “includes,” “having,” or “has” an element or a plurality of elements having a particular property may include additional such elements not having that property. In the appended claims, the terms “including” and “in which” are used as the plain-English equivalents of the respective terms “comprising” and “wherein.” Moreover, in the following claims, the terms “first,” “second,” and “third,” and the like are used merely as labels, and do not impose numerical requirements on their objects. Further, the limitations of the following claims are not written in means-plus-function format and are not intended to be interpreted based on 35 U.S.C. § 112(f), unless and until such claim limitations expressly use the phrase “means for” followed by a statement of function devoid of further structure.
The above description is illustrative, and not restrictive. For example, the above-described embodiments (and/or aspects thereof) may be used in combination with each other. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the subject matter without departing from its scope. While the dimensions and types of materials described herein define the parameters of the subject matter, they are exemplary embodiments. The scope of the subject matter should, therefore, be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled.
This written description uses examples to disclose several embodiments of the subject matter, including the best mode, and to enable one of ordinary skill in the art to practice the embodiments of subject matter, including making and using any devices or systems and performing any incorporated methods. The patentable scope of the subject matter is defined by the claims, and may include other examples that occur to one of ordinary skill in the art. Such other examples are intended to be within the scope of the claims if they have structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements with insubstantial differences from the literal languages of the claims.
| Number | Date | Country | Kind |
|---|---|---|---|
| 102017000050006 | May 2017 | IT | national |
This application claims priority to and is a continuation-in-part of U.S. patent application Ser. No. 16/611,896 (filed 8 Nov. 2019), which is a 35 U.S.C. § 371 national stage entry of International Patent Application No. PCT/IB2018/053223 (filed 09May-2018), which claims priority to Italian Patent Application No. 102017000050006 (filed 9 May 2017). The entire disclosures of these patent applications are incorporated herein by reference.
| Number | Date | Country | |
|---|---|---|---|
| Parent | 16611896 | Nov 2019 | US |
| Child | 17979714 | US |
