Automated detection of wear or failure of components based on vibrations

Abstract

TITLE: Automated Vibration-Based Component Wear or Failure Detection System and method for detecting component abnormalities of a vehicle using vibration detection. The system includes a first sensor positioned at a first position on the vehicle and detecting vehicle vibration and an electronic processor connected to the first sensor. The processor receives information from the first sensor produced by a detected vibration of the vehicle. The electronic processor determines a vibration pattern from the sensor information. The processor is configured to determine whether a component abnormality exists from the vibration pattern. In response to determining that a component abnormality exists, the electronic processor performs an action to address the component abnormality. Figure 1

Description

Title of the invention: Automated detection of wear or failure of components based on vibrations FIELD OF THE INVENTION

[0001] The present invention relates to the automated detection of wear or failure of components based on vibrations. STATE OF THE ART

[0002] The driver of a vehicle detects component abnormalities or subsystem failures of the vehicle, including the transmission, suspension, wheel balance, wheel alignment, brake rotors, wheel bearings, tie rods, exhaust, engine, or similar subassemblies. The driver observes these abnormalities and failures based on vibration noise or harshness (NVH characteristics) within the passenger compartment. Some self-driving vehicles may be used as taxis or for shared uses. In both cases, there is no regular occupant or driver of the vehicle. In some cases, fully autonomous taxis transport passengers without a driver present in the vehicle. During operation, components experience wear or may fail. Brief description of the drawings

[0003] The present invention will be described hereinafter in more detail with the aid of the embodiment shown in the accompanying drawings, in which:

[0004] [Fig. 1] Block diagram of a vehicle control system corresponding to certain examples,

[0005] [Fig.2] diagram of the electronic control of the system of [Fig.l], according to some examples,

[0006] [Fig.3] flowchart of an example of a method for detecting wear and component failure,

[0007] [Fig.4] Block diagram of a vehicle control system,

[0008] [Fig.5] flowchart of an example of a method for detecting wear and component failure.

[0009] DETAILED DESCRIPTION OF EMBODIMENTS

[0010] When a vehicle is driven on the road, it may experience component failures resulting from natural wear and tear, damage, or other circumstances. Fully autonomous taxis transport passengers without the presence of a driver in the vehicle. In addition, fully or partially autonomous vehicles are part of a fleet and are used in a shared manner. As a result, the occupant of the vehicle or its driver does not use the vehicle regularly enough to detect anomalies or component failures. Vehicles are equipped with sensors to detect the vehicle's condition.

[0011] Even if the risk of affecting the technical operation of the vehicle is minor, it is desirable to remedy such wear before it causes more significant difficulties. In addition, certain noises, vibrations or harshness, in reaction, may be the signal of a more serious or foreseeable difficulty, rendering the vehicle unusable. Without the presence of a driver or a person who determines and acts on the cause of such a reaction, it is desirable that the autonomous vehicle can detect such incidents automatically. Thus, the invention relates to systems and methods for detecting, classifying and mitigating such wear or component failures for vehicle systems including autonomous driving systems.

[0012] Examples described are systems using vibration patterns (e.g., detecting the use of an accelerometer or other type of vibration sensor) or other input from sensors to detect wear or failure of a component. With such examples, preventive measures can be taken which, if necessary, are based on a detected anomaly. For example, the vehicle can indicate to its user (e.g., the fleet operator) to contact the appropriate authorities based on the nature of the component anomaly. Similarly, the vehicle can leave the route and reach an operational center for further investigation and taking appropriate action to remedy the component anomaly.

[0013] An exemplary embodiment of a system is for detecting abnormalities of a vehicle component. The system includes a first sensor positioned in a first position on the vehicle and configured to detect vibrations of the vehicle; an electronic processor is cooperatively coupled with the first sensor. The electronic processor is configured to receive from the first sensor sensor information produced by the detected vibration of the vehicle. The electronic processor is configured to determine a vibration pattern based on the information provided by the sensor. The electronic processor is configured to determine based on the vibration pattern whether there is an abnormality of the component. The electronic processor is configured to perform a mitigating action based on the abnormality of the component if it has determined that there is a component abnormality.

[0014] Another embodiment relates to a method for detecting anomalies in components of a vehicle. This method comprises receiving from a first sensor placed in a first position in the vehicle, sensor information produced by the detected vibration of the vehicle. The method comprises comparing with a processor electronics cooperating with the first sensor, the sensor information relating to the vibration noise level to extract one or more vibrations that exceed the vibration noise level. This method comprises generating a vibration pattern based on one or more vibrations that exceed the vibration noise level. The method comprises determining from the vibration pattern whether there is a component abnormality. The method comprises in response to determining that there is a component abnormality, performing a mitigation action based on the component abnormality.

[0015] The term "component anomaly" as used herein relates to either a component failure or a condition of a vehicle component, system or subsystem that is outside the acceptance range of the component, system or subsystem.Examples of component abnormalities include: drivetrain abnormalities (e.g., aging universal joint, low fluid level, or torque converter failure), suspension abnormalities (e.g., worn shock absorbers, ball joints, sway bar mounts, and control arm bearings), wheel imbalance, improper wheel alignment, warped brake rotors, failed wheel bearings, tie rods, exhaust system defects (e.g., leaking or failed mufflers), or engine abnormalities (e.g., worn or improperly installed drive belt or worn engine mounts).

[0016] Before proceeding to the detailed description of the invention, it should be noted that the invention is not limited in its application to construction details and arrangement of components as indicated in the following description and as shown in the drawings. The invention may be applied in various ways.

[0017] It should be noted that all circuits and devices using programs as well as all the different structural components can be used to implement the invention. Furthermore, it should be noted that the examples presented may include circuits, programs and electronic components and modules which for presentation are represented and described as if most of the components were implemented only in circuit form. However, reading the description shows that in at least one embodiment, the presentation of the invention based on electronics can be implemented in the form of a program (for example, recorded on a non-volatile, computer-readable medium) and which will be executed by one or more processors.It should also be noted that a set of circuits and devices using programs as well as a set of different structural components can be used to carry out the invention. For example, the "control units" and "controls" described in the description may comprise one or more . electronic processors, one or more physical memory modules with a non-volatile, computer-readable medium, one or more input / output interfaces and various connections (e.g. a system bus) connecting the components.

[0018] For ease of description, some or all of the exemplary systems are presented by a single example of each component. Some examples may not describe or show all of the components of the systems. Other examples include a greater or lesser number of each of the components shown and may combine some components or include additional components or component variations.

[0019] [Fig. 1] is a block diagram of an exemplary autonomous vehicle control system 100. As described in more detail below, the autonomous vehicle control system 100 may be installed on or integrated into a vehicle 102 and autonomously drive the vehicle. In the following description, the terms "autonomous vehicle" and "automated vehicle" are not limiting. The terms are used generally to refer to a vehicle operating autonomously or automatically and having varying degrees of automation (i.e., the vehicle is configured to drive itself or in some cases without driver intervention). The system and methods described may be used on any vehicle, capable of operating partially or fully autonomously or manually controlled by a driver, or a combination of these modes of operation.The term "driver" as used herein generally refers to the occupant of an autonomous vehicle who is seated in the driver's seat, operates the vehicle's controls when in manual mode, or provides control inputs to the vehicle to influence the autonomous operation of the vehicle.

[0020] In the example presented, the system 100 comprises an electronic controller 104, a vehicle control system 106, the sensors 108, a vibration sensor 110, a GNSS (Global Navigation Satellite System) satellite navigation system 112, a transmitter 114, a human machine interface (HMI) 116. The components of the system 100 with other modules and components are electrically connected to each other by one or more control and data buses (for example the bus 118) which allows communication between the components. The use of control and data buses for interconnection and control between the different modules and components is in principle known to those skilled in the art. In some cases, the bus 118 is a CAN bus (CAN data bus). In some cases the bus 118 is an Ethernet or FlexRay communication bus or other suitable wired buses.According to alternative embodiments, some or all of the components of the system 100 communicate with each other by modes of . wireless communication (e.g., Bluetooth or near field communication). For ease of description, the system 100 shown in [Fig.l] includes one of each of the above components. Alternatively, embodiments may include one or more of the components or may not have or combine certain components.

[0021] The electronic controller 104 (described in more detail below with the aid of [Fig. 2]) manages the vehicle control systems 106 and the sensors 108 for the fully or partially autonomous control of the vehicle. The electronic controller 104 receives telemetry elements from the sensors 108 and determines control data and commands for the vehicle. The electronic controller 104 transmits the vehicle control data, among other things, to the vehicle control systems 106 for driving the vehicle (e.g., by generating braking signals, acceleration signals and steering signals).

[0022] The vehicle control systems 106 include controllers, sensors, actuators or the like for controlling the various operating parts of the vehicle 102 (e.g., steering, acceleration, braking, gear shifting or the like). The vehicle control systems 106 are configured to transmit and receive data regarding the operation of the vehicle 102, exchanged with the electronic controller 104.

[0023] The sensors 108 determine one or more parameters of the vehicle and its environment and communicate information regarding such parameters to the other components of the system 100 using, for example, electrical signals. The vehicle parameters include, for example, the position of the vehicle or parts or components of the vehicle, the movement of the vehicle or parts or components of the vehicle, the forces acting on the vehicle or parts or components of the vehicle, the proximity of the vehicle to other vehicles or objects (stationary or moving), the yaw rate, the sideslip angle, the steering angle, the overlap angle, the vehicle speed, the longitudinal acceleration and the lateral acceleration or other parameters.The sensors 108 include, for example, vehicle control sensors (e.g., sensors detecting accelerator pedal position, brake pedal position, steering wheel position (steering angle)), wheel speed sensors, vehicle speed sensors, yaw rate sensors, force sensors, odometer sensors, vehicle proximity sensors (e.g., camera, lidar radar, and ultrasonic sensors). In some cases, the sensors 108 include one or more cameras configured to capture one or more images of the environment of the vehicle 102, according to the different fields of view. The cameras may include . multiple types of imaging / sensor arrangements each of which is located in a different position inside or outside the vehicle 102.

[0024] The vibration sensor 110 is a transducer for detecting vibrations of a vehicle component, converting the vibrations into electrical signals, and transmitting the electrical signals to the electronic controller 104. In some cases, the vibration sensor 110 is an accelerometer. In some cases, the vibration sensor may be a strain gauge, a leakage current sensor, a gyroscope, a microphone, or other vibration sensor. In some cases, the vibration sensor 110 is integrated into another sensor of the vehicle (for example, it is combined with a vehicle wheel speed sensor 102). In some cases, multiple vibration sensors are used, for example, mounted on each of the vehicle wheels or at different points on the vehicle chassis. In some cases, the vibration sensor 110 is implemented as a micromechanical-electrical system (MEMS).As described, the electronic controller 104 processes the electronic signals received from the vibration sensor 110 to produce a vibration pattern that is analyzed to determine a component anomaly that is causing the vibrations. In some cases, the vibration sensor 110 includes an on-board signal processing circuit that produces and transmits the sensor information including the vibration patterns to the electronic controller 104 for processing.

[0025] The electronic controller 104 receives and interprets the signals received from the sensors 108 and the vibration sensor 110 to automatically detect wear or failure of any component of the vehicle.

[0026] In some cases, the system 100 includes, in addition to the sensors 108, a GNSS satellite navigation system 112. The GNSS navigation system 112 receives radio frequency signals from orbiting satellites, using one or more antennas and receivers (not shown). The GNSS system 112 determines the geospatial position (i.e., latitude, longitude, altitude, and speed) of the vehicle from the received radio frequency signals. The GNSS system 112 communicates its position information to the electronic controller 104. The electronic controller 104 uses this information in combination with or instead of information received from some of the sensors 108 when controlling the autonomous vehicle 102.

[0027] The transmitter 114 comprises a radio transmitter communicating the data via wireless or wired communication networks (e.g., cellular networks, satellite networks, land mobile radio networks, etc.) including the communication network 120. The communication network 120 is a network comprising wireless connections, wired connections, or a combination of both. The communication network 120 is implemented using a very wide range of networks, for example, the Internet (including the public Internet and the private Internet). An LTE network, a Global System for Mobile Communications (GSM) network, a Code Division Multiple Access (CDMA) network, an Evolution Data Optimized (EV-DO) network, an EDGE network, a 3G network, a 4G network, a 5G network and one or more local area networks, for example, Bluetooth, Wi-Fi or a combination or derivatives of these networks.

[0028] The transmitter 114 provides wireless communications within the vehicle using appropriate network modalities (e.g., Bluetooth, Near Field Communication (NFC), Wi-Fi, or the like). Thus, the transmitter 114 is communicatively connected to the electronic controller 104 and other components of the system 100 by networks or electronic devices, or both, inside and outside the vehicle 102. For example, the electronic controller 104 using the transmitter 114 may communicate with the fleet operator 122 for the motor vehicle 102 to send and receive data, commands, or other information (e.g., notifications of component abnormalities). In another example, the electronic controller 104 using the transmitter 114 may contact emergency services (e.g., public service agencies (PSAPs) 124) using 911 (E911) communication modalities.The transmitter 114 includes other wireless communications components (e.g., amplifiers, antennas, baseband processors, or the like) that are not detailed herein and may be implemented as electronic circuitry or software, or a combination of both. In some cases, multiple transmitters or separate transmitting and receiving components (e.g., a transmitter and a receiver) are provided instead of a transmitter that combines both functions.

[0029] The HMI 116 provides visual output such as, for example, graphical indicators (e.g., fixed or animated icons), lights, colors, text, images, or combinations thereof. The HMI 116 includes a display mechanism suitable for displaying the visual output such as, for example, an instrument cluster, a mirror, a head-up display, a display screen on the center console (e.g., a liquid crystal display (LCD), a touchscreen, or an organic light-emissive diode (OLED)), or other suitable mechanisms. In alternative embodiments, the display screen is not a touchscreen. In some cases, the HMI 116 displays a graphical user interface (GUI) (e.g., generated by the electronic controller and presented on a display screen) that allows the driver or passenger to interact with the autonomous vehicle 102.The HMI 116 may also provide audio output to the driver, such as a chime, buzzer, voice output, or the like, provided by a speaker integrated into the HMI 116 or separate from the HMI 116. In some cases, the HMI 116 provides haptic outputs to the driver. by vibrating one or more vehicle components (e.g., the steering wheel or seats), for example, by using a vibrator. In some cases, the HMI 116 provides a combination of audio, visual, and haptic outputs.

[0030] The controller 104 using the transmitter 114 communicates with a mobile electronic device 126. According to variants, the mobile electronic device 126 when it is near or inside the autonomous vehicle 102 can be communicatively coupled with the electronic controller 104 by a wired connection using for example a universal serial bus (USB) or other similar connections. The mobile electronic device 126 is for example a smartphone, a tablet, a personal digital assistant (PDA), a smart watch or other portable or wearable electronic device and which includes a modem or can be connected to a network modem or similar components enabling wireless or wired communications (for example, a processor, a memory, an interface, a transmitter, an antenna or similar means).The HMI interface 116 may communicate with the mobile electronic device 126 to provide visual, audio, and haptic outputs by the mobile electronic device 126 when the device 126 is communicatively coupled to the autonomous vehicle 102.

[0031] [Fig. 2] provides an example of an electronic controller 104 comprising an electronic processor 205 (e.g., a microprocessor, a special application integrated circuit, etc.), a memory 210, and an input / output interface 215. The memory is comprised of one or more non-volatile, computer-readable media with at least one program storage area and one data storage area. The program storage area and the data storage area comprise a combination of different types of memory such as read-only memory (ROM), random access memory (RAM), (e.g., dynamic random access memory (DRAM), synchronous DRAM (SDRAM), etc.), erasable read-only memory (EEPROM), flash memory, or other suitable memory devices. The electronic processor 205 is coupled to the memory 210 and the input / output interface 215.The electronic processor 205 sends and receives information (e.g., memory 210 and / or input / output interface 215) and processes the information by executing one or more instructions or program modules that may be stored in the memory 210 or other non-volatile, computer-readable medium. The program may include firmware, one or more applications, program data, filters, rules, one or more program modules, or other executable instructions. The electronic processor 205 is configured to retrieve the memory 210 and execute, among other things, programs for autonomous and semi-autonomous control of the vehicle and to perform the methods described. In the embodiment shown, the memory 210. contains among other things a vibration detection algorithm 220 which operates as described to detect vibrations, classify vibration patterns to identify component anomalies.

[0032] The input / output interface 215 transmits and receives information from devices external to the electronic controller 104 (e.g., via one or more wired or wireless connections), e.g., components of the system 100 via the bus 118. The input / output interface 215 receives input (e.g., from sensors 108, from the HMI 116, etc.) and provides system outputs (e.g., to the HMI 116, etc.) or a combination thereof. The input / output interface 215 includes one or more input and output mechanisms that will not be detailed and that may be implemented as program circuitry or a combination of both.

[0033] In some cases, the electronic controller 104 uses one or more machine learning methods to analyze the vibration data to identify component anomalies (as described). Machine learning generally relates to the ability of a computer program to learn without being explicitly programmed. In some cases, a computer program (e.g., a learning machine) is configured to build an algorithm based on the inputs. Supervising the learning involves presenting a computer program with example inputs and desired outputs. The computer program is configured to learn a general rule that maps inputs to outputs from the training data it receives.Examples of machine learning include decision tree learning, rule association learning, neural networks, classifiers, inductive logic programming, support vector machines, grids, Bayesian networks, reinforcement learning, representation learning, metric and similarity learning, sparse dictionary learning, and genetic algorithms. Using such solutions, the computer program can receive sparse data and understand data and gradually refine the algorithms for data analysis.

[0034] It is noted that although [Fig. 2] shows a single electronic processor 205, a single memory 210 and a single input / output interface 215, alternative embodiments of the electronic controller 104 may include multiple processors, multiple memory modules and / or multiple input / output interfaces. It is also noted that the system 100 may include other electronic controllers, each having similar components and configured similarly to the electronic controller 104. In some cases, the electronic controller 104 is implemented in part or in whole in the form of a semiconductor (e.g., a network (FPGA / programmable logic circuit). Similarly, the individual modules and controllers as described can be implemented as separate controllers or as components of a single controller. In some cases, a combination of these solutions is also possible.

[0035] [Fig. 3] shows an exemplary method 300 for automatically detecting, classifying and / or mitigating component anomalies. Although the method 300 is described in connection with the system 100 as shown, the method 300 may also be applicable to other systems and other vehicles. The method 300 may also be modified or executed differently from the example shown. As an example, the method 300 is described as executed by the electronic controller 104 and in particular the electronic processor 205. However, in some cases, portions of the method 300 may be executed by other devices or subsystems of the system 100.

[0036] In block 302, the electronic processor 205 receives sensor information from the first sensor (e.g., the vibration sensor 110) located in a first position on the vehicle and configured to detect vibrations of the vehicle. The electronic processor 205 receives signals (via, for example, a CAN bus) from an accelerometer located on a wheel of the vehicle. The electronic processor 205 continuously receives the sensor information according to one example. In some cases, the electronic processor 205 receives periodic bursts of vibration sensor 110 information. The sensor information is stored in a buffer or other memory of the electronic controller 104 until it is processed.

[0037] In block 304, the electronic processor 205 determines a vibration pattern based on the sensor information. In some cases, the vibration pattern is determined by taking an example of the sensor information. In some cases, the electronic processor 205 compares the sensor information to a vibration noise level to extract one or more vibrations exceeding the vibration noise level. In some cases, the vibration noise level is a predefined value set by the vehicle manufacturer. In some cases, the vibration noise level is set by the electronic processor 205 based on the operation of the vehicle over time. For example, the electronic processor 205 may periodically sample vibration information during normal operation of the vehicle and take the average of the examples to set the vibration noise level.In some cases, the vibration noise level value is adjusted based on the current operating state of the vehicle. For example, the current vehicle speed and acceleration can be used to adjust the noise level by increasing or decreasing it to compensate for the vibrations added by the vehicle's operation. vehicle. In some cases, the vibration noise level is determined continuously, using sensor information from the other vibration sensors. For example, for a vehicle having a vibration sensor on each wheel, the electronic processor 205 averages the readings from all four sensors to determine the vibration noise level. In another example, the electronic processor 205 averages the readings from the sensors that are not used to produce the vibration pattern, so as to determine the vibration noise level.

[0038] Regardless of how the vibration noise level is determined, the electronic processor 205 generates the vibration pattern based on one or more vibrations exceeding the vibration noise level.

[0039] In block 306, the electronic processor 205 determines whether a component has an anomaly based on the vibration pattern. For example, the electronic processor 205 uses a pattern matching algorithm to determine whether the vibration event matches a known vibration pattern associated with an anomaly of a particular component. In some cases, the electronic processor 205 determines whether a component anomaly exists based on the vibration pattern and one or more vehicle parameters (e.g., received from one or more vehicle control systems 106 or sensors 108). For example, certain types of vibration are more indicative of a failure of a particular component when those vibrations occur during braking (e.g., warped rotors) or steering (e.g., worn tie rods).The electronic processor 205 may determine several vehicle parameters for a period beginning just before the vibration pattern begins and ending just after the vibration pattern ends (e.g., five seconds before and after the occurrence of the vibration pattern).

[0040] In some cases, the electronic processor 205 determines that there is a component anomaly classifying the vibration pattern using the machine learning algorithm (e.g., a neural network or a classifier) executable by the electronic processor 205. In some cases, the machine learning algorithm is trained using historical component anomaly data. For example, the machine learning algorithm is fed training data that includes example inputs (e.g., vibration pattern data representing the anomalies of a particular component) and corresponding desired outputs (e.g., indications of component anomalies). The training data may also include metadata for the vibration patterns.Metadata includes for example the vehicle speed at the time of the vibration patterns, the vehicle model in which the vibration pattern was detected, the vehicle state at the time of the . vibration pattern (e.g., braking, acceleration, cornering, etc.) and the environmental conditions at the time of the vibration pattern (e.g., ambient temperature, ambient humidity, atmospheric conditions, road conditions, etc.). By processing the training data, the machine learning algorithm gradually develops a predictive model that maps the inputs to the outputs contained in the training data.

[0041] In some cases, the vibration pattern is fed into the machine learning algorithm that identifies the cause of the component anomaly. In some cases, the machine learning algorithm generates multiple potential component anomalies based on the vibration data and determines for each potential component anomaly a confidence score. A confidence score indicates the likelihood that a potential component anomaly is the cause of the vibration pattern (e.g., how closely the detected vibration pattern approximates vibration patterns for the same type of potential component anomaly). In such embodiments, the electronic processor 205 selects the component anomaly from a set of potential component anomalies based on the confidence score. For example, the potential component anomaly with the highest confidence score will be chosen.In some cases, a confidence score is a numerical representation (e.g., between 0 and 1) of confidence. For example, the vibration pattern may have a 60% agreement with one potential component anomaly but may have an 80% agreement with another potential component anomaly, resulting in a confidence score of 0.6 and a confidence score of 0.8, respectively.

[0042] Optionally, in some cases, the electronic processor 205 assigns a weight to one or more potential component anomalies by relying on metadata for the vibration pattern and the potential component anomaly and choosing the component anomaly from a set of potential component anomalies based on the confidence score and the weight.

[0043] The weight is used to indicate the significance level of a particular piece of metadata in identifying a potential component anomaly as a relative component anomaly compared to other potential component anomaly. For example, when both the vehicle experiences a component anomaly and the vehicle provided the training data for the potential component anomaly corresponding to the same model, then the potential component anomaly may be given a higher weight than if it were assigned in the case where the metadata indicates two different models of vehicles. In another example, the vehicle has a component anomaly for acceleration and the vehicle produced the training data for the potential component anomaly that corresponded to the deceleration, then the potential component anomaly may be assigned a lower weight than if the metadata indicated that both vehicles were accelerating. Higher weight metadata contributes more to the confidence level. For example, a small amount of high-weight metadata may result in a higher confidence score than a larger amount of lower-weight metadata. In such embodiments, the electronic processor 205 determines for each of the set of potential component anomalies a weighted confidence score based on the confidence score and the weight (weighting). For example, the electronic processor 205 may multiply the confidence scores by the assigned weight. In these embodiments, the electronic processor 205 chooses the component anomaly from the set of potential component anomalies based on the weighted confidence score.For example, we will choose the component anomaly with the highest weighted confidence score.

[0044] In some cases, the weights are statistically predetermined for each type of metadata. In some cases, the weights may be determined using the machine learning algorithm. Over time, as matches for vibration patterns are determined and confirmed or rejected by observation, the machine learning algorithm may determine that particular metadata is more determinative than others for a high confidence score and thus increases the weight corresponding to its metadata.

[0045] As shown in [Fig. 3], when the electronic processor 205 does not determine (in block 306) that there has been a component anomaly (e.g., the vibration pattern does not match a known component anomaly), the electronic processor 205 continues to receive (in block 302) and process sensor data to detect component anomalies. In some cases, the electronic processor 205 is configured to continuously detect and classify component anomalies. In other embodiments, the electronic processor 205 is configured to apply the method 300 periodically to detect the component anomalies.

[0046] Regardless of how component anomalies are determined, in block 308, the electronic processor 205 performs a mitigating action based on the component anomaly. In some cases, the mitigating action includes transmitting (e.g., by the transmitter 114) a notification to the fleet operator. For example, an appropriate API or network message may be used to send a notification indicating that a component anomaly has occurred, along with the time and location of the component anomaly, the type of component anomaly, or otherwise. The fleet operator responds to receiving the notification by sending commands to the electronic controller 104. to guide the vehicle to a fleet maintenance facility, to drive the vehicle safely out of traffic (if necessary) until another vehicle can be dispatched to the passenger(s) etc.

[0047] In some cases, the mitigating action involves transmitting (e.g., by transmitter 114) a notification to a public safety agency, for example, in the case of a potential hazard issue causing the vibration pattern, then electronic processor 205 sends an alert corresponding to the information regarding the vehicle in distress or other information using, for example, the E911 system (a service routing emergency calls over a VoIP network to a public safety answering point).

[0048] In some cases, the mitigating action is to control the vehicle to exit traffic. For example, if the component fault is more severe, the electronic controller 104 may automatically control the vehicle to exit traffic and enter a parking lot or other location relatively outside of vehicle traffic. In some cases, the electronic controller 104 may automatically operate the vehicle to drive it to the maintenance facility.

[0049] In some cases, the mitigation action includes an alert sent to a vehicle human / machine interface to inform the passenger of the component abnormality and any other remedy (mitigation action taken). For example, the display of the HMI 116 shows a message such as "Vehicle brake requires service. We are going to a service facility for follow-up" or "The vehicle wheels are not aligned. The vehicle operator is alerted and the maximum speed of the vehicle will be reduced until the problem is addressed." In some cases, the HMI 116 may voice-activate the alert to the vehicle passenger. In some cases, the alerts are combined. In some cases, the electronic processor 205 sends an alert to a mobile electronic device of the passenger (e.g., using the transmitter 114).

[0050] In some cases, multiple remedy actions are combined.

[0051] [Fig. 4] is a block diagram of an exemplary autonomous vehicle control system 400. In the system 400, the electronic controller 104 receives sensor information from one or more accelerometers 110 and one or more vehicle status or condition inputs 402. As described, the electronic controller 104 uses the sensor information and the vehicle status or vehicle parameter inputs to detect component abnormalities and transmit the abnormalities to various attenuation outputs 404 (using the transmitter 114), the HMI 116, or both.

[0052] [Fig.5] shows an example method 500 for automatically detecting, classifying and / or remedying vehicle component anomalies. Although the method 500 is described in connection with the systems 100 and 400 as described, the method 500 may also be used by other systems and vehicles. Furthermore, the method 500 may be modified or performed differently from the example described. By way of example, the method 500 is described as being applied by the electronic controller 104 and in particular the electronic processor 205. However, in some cases, portions of the method 500 may be applied by other devices or subsystems of the systems 100 and 400.

[0053] In block 502, electronic processor 205 collects and compares accelerometer measurements to determine a vibration pattern as described.

[0054] In block 504, the electronic processor 205 determines whether the vibration pattern is a repeating pattern (i.e., one that has occurred more than once). For example, the electronic processor 205 may compare the current vibration pattern to a library of detected vibration patterns stored in a memory of the electronic controller 104. In some cases, the vibration pattern exceeds a threshold repetition value before the electronic processor 205 determines that it is a recurring vibration pattern. For example, in some cases, the vibration pattern may have occurred three or more times to be considered a repetition. In block 506, if the vibration pattern is not a repetition, the electronic processor 205 stores the vibration pattern (e.g., in memory 210) for comparison with future detected vibration patterns and ignores the vibration pattern (in block 508).In some cases, if a vibration pattern is ignored, the electronic processor 205 continues analyzing vibration sensor information (in block 502).

[0055] In block 510, in response to determining that the vibration pattern is a repeating vibration pattern, the electronic processor 205 determines whether the vibration event correlates with a previously recorded vibration event. The term "vibration event" used represents a detected, recurring vibration pattern combined with metadata associated with the vibration pattern. In some cases, the metadata includes current vehicle system data for a time frame, including the time at which the recurring vibration pattern was sent. The vehicle system data may include vehicle status input values 402, status or control values for the vehicle system 106, sensor inputs 108, or combinations of such information.In some cases, the electronic processor 205 determines whether a vibration event correlates with a previously recorded vibration event using techniques similar to those described with respect to the method 300 and to determine whether a component anomaly exists.

[0056] In some cases, the electronic processor combines the functions of blocks 504 and 510 to check for recurring vibration events rather than a first recurring vibration pattern check. For example, each time a vibration pattern is detected, the metadata is combined to create a vibration event which is then checked for recurrence before processing in block 516.

[0057] In block 512, when a vibration event does not correlate with a previously recorded vibration event, the electronic processor 205 records the vibration event as a new event (e.g., in memory 210) and ignores the vibration event (in block 514). In some cases, if the vibration event is ignored, the electronic processor 205 continues analyzing the sensor information regarding the vibration pattern and possible vibration events (in block 502).

[0058] In block 516, if the vibration event does not correlate with a previously recorded vibration event, the electronic processor 205 determines whether the vibration event is a sensor-specific event (i.e., the vibration pattern was detected only once by many vibration sensors). For example, the electronic processor 205 compares data from multiple accelerometers 110 to determine whether the vibration pattern comprising the vibration event is detected on only one or more accelerometers 110. In block 518, if the vibration event is sensor-specific, the electronic processor 205 correlates the vibration pattern to a specific wheel anomaly (e.g., the wheel with which the sensor is associated). In block 520, the electronic processor 205 determines whether the vibration pattern corresponds to a particular type of component anomaly (as described).In block 522, if there is no match then the vibration event is ignored. In some cases, if a vibration event is ignored, the electronic processor 205 continues analyzing the sensor information for vibration patterns and possible vibration events (in block 502). In block 524, if there is no match then the anomaly is received and remedial action is taken (e.g., sending an alert).

[0059] In block 526, if the event is not sensor-specific (i.e., if the vibration pattern is detected by more than one of many vibration sensors), the electronic processor 205 correlates the source of the vibration pattern and the vehicle chassis (e.g., alignment, transmission, engine, exhaust, or the like). In block 528, the electronic processor 205 determines whether the vibration pattern matches a particular type of component anomaly (as described). In block 530, if there is no match, the event is acknowledged and an alert is sent regarding an unknown or unspecified solution. potential for the vehicle. In block 524, if there is no match, the anomaly is taken into account and a corrective action is taken (for example, sending an alert).

[0060] Thus, the described embodiments provide, among other things, an autonomous vehicle control system configured to detect and remedy component anomalies.

[0061] It should be emphasized that according to an advantageous characteristic, the electronic processor 205 is configured to classify the vibration pattern using the machine learning algorithm into:

[0062] - generating a set of potential component anomalies based on the pattern of vibration, and

[0063] - determining for each of the potential component anomalies a score of confidence, and

[0064] - choosing the component anomaly from the set of anomalies component potentials based on confidence scores.

[0065] According to another advantageous characteristic, the system further comprises:

[0066] - a second sensor 110 placed in a second position on the vehicle and configured to detect vehicle vibrations, the electronic processor 205 being communicatively coupled to the second sensor 110 and, further, it is configured to:

[0067] * receive from the second sensor 110, additional sensor information produced by the detected vibration of the vehicle, and

[0068] * determine the vibration pattern based on the sensor information and additional sensor information.

[0069] In this system, the electronic processor is further configured to:

[0070] - before determining whether there is a component anomaly, determine whether the pattern of vibration is recurrent, and

[0071] - determining whether there is a component anomaly in response to determining that the vibration pattern is recurring.

[0072] Particularly in the system, the action to remedy is at least one of the group of actions consisting of transmitting a notification to a vehicle owner, transmitting a notification to a fleet operator, transmitting a notification to a vehicle manufacturer, transmitting a notification to a public safety agency, commanding the vehicle to leave traffic and producing an alert on a human machine interface of the vehicle.

[0073] According to another advantageous characteristic, the vehicle parameter is at least one of the parameters chosen from the group comprising: vehicle speed, wheel speed, steering angle, throttle level, braking level, gear selection and temperature.

[0074] According to another advantageous characteristic, the method further consists of:

[0075] - receive from the second sensor, placed in a second position on the vehicle, a additional sensor information produced by the detected vehicle vibration, and

[0076] - determine the vibration pattern based on the sensor information and additional sensor information

[0077] Additionally according to the method, before determining whether there is a component abnormality, determining whether the vibration pattern is recurring, and determining whether there is a component abnormality in response to determining that the vibration pattern is recurring.

[0078] According to another characteristic of the method, performing the action to reduce consists of performing at least one of the actions selected from the group consisting of transmitting a notification to a vehicle owner, transmitting a notification to a fleet operator, transmitting a notification to a vehicle manufacturer, transmitting a notification to a public safety agency, commanding the vehicle to leave traffic and producing an alert on the vehicle's human-machine interface.

[0079] According to the method, determining the vehicle parameter comprises determining the vehicle parameter comprises determining at least one parameter selected from the group comprising: vehicle speed, wheel speed, steering angle, throttle level, braking level, gear selection and temperature.

Claims

Claims

1. A system for detecting vehicle component anomalies, the system (100) comprising: - a first sensor (110) placed in a first position on the vehicle and configured to detect vibrations of the vehicle, - an electronic processor (205) communicatively coupled to the first sensor (110) and configured to * receive from the first sensor (110) sensor information produced by a detected vibration of the vehicle, * determine a vibration pattern from the sensor information, - determine a vehicle parameter, * determine with the vibration pattern and the vehicle parameter whether there is a component anomaly, and * in response to determining the existence of a component anomaly, perform an action to remedy based on the component anomaly, the vehicle parameter is selected from the following parameters: the position of the vehicle or parts of components of the vehicle,the movement of the vehicle or parts or components of the vehicle, the forces acting on the vehicle or parts or components of the vehicle, the proximity of the vehicle to other vehicles or objects (fixed or moving), the yaw rate, the sideslip angle, the steering angle, the overlap angle, the vehicle speed, the longitudinal acceleration and the lateral acceleration.,

2. The system of claim 1, wherein the electronic processor (205) is configured to determine the vibration pattern by: - comparing the sensor information to a vibration noise level to extract one or more vibrations that exceed the vibration noise level, and - generating the vibration pattern based on one or more vibrations that exceed the vibration noise level.

3. The system of claim 1, wherein the electronic processor (205) is configured to determine whether a component anomaly exists by classifying the vibration pattern with a machine learning algorithm.

4. The system of claim 3, wherein the machine learning algorithm is trained by historical component anomaly data.

5. The system of claim 4, wherein the electronic processor (205) is further configured to classify the vibration pattern using the machine learning algorithm by: - generating a set of potential component anomalies based on the vibration pattern, and - determining for each of the potential component anomalies a confidence score, and - choosing the component anomaly from the set of potential component anomalies based on the confidence scores.

6. The system of claim 5, wherein the electronic processor is further configured to: - assign a weight to each of the set of potential component anomalies based on metadata for the potential component anomaly, and - select the component anomaly from the set of potential component anomalies based on the confidence score and the weight.

7. The system of claim 1, further comprising: - a second sensor (110) positioned in a second position on the vehicle and configured to detect vibrations of the vehicle, the electronic processor (205) being communicatively coupled to the second sensor (110) and, further, it is configured to: * receive from the second sensor (110), additional sensor information produced by the detected vibration of the vehicle, and * determine the vibration pattern based on the sensor information and the additional sensor information.

8. The system of claim 1, wherein the electronic processor is further configured to: - before determining whether a component anomaly exists, determine whether the vibration pattern is recurring, and - determine whether a component anomaly exists in response to determining that the vibration pattern is recurring.

9. The system of claim 1, wherein the action to remediate is at least one of the group consisting of transmitting a notification to a vehicle owner, transmitting a notification to a fleet operator, transmitting a notification to a vehicle manufacturer, transmitting a notification to a public safety agency, commanding the vehicle to exit traffic, and generating an alert on a human-machine interface of the vehicle.

10. The system of claim 1, wherein the first sensor is an accelerometer.

11. The system of claim 1, wherein the vehicle parameter is at least one of the parameters selected from the group consisting of: vehicle speed, wheel speed, steering angle, throttle level, brake level, gear selection and temperature.

12. A method of detecting component anomalies of a vehicle, the method comprising: - receiving from a first sensor (110) placed in a first position on the vehicle, sensor information produced by the detected vibration of the vehicle, - comparing with an electronic processor communicatively coupled to the first sensor, the sensor information with a vibration noise level to extract one or more vibrations exceeding the vibration noise level, - generating a vibration pattern based on one or more vibrations exceeding the vibration noise level, - determining a parameter of the vehicle, - determining based on the vibration pattern and the vehicle parameter whether there is a component anomaly, and - in response to determining the existence of a component anomaly, performing an action to remedy it based on the component anomaly, the vehicle parameter is selected from the following parameters: the position of the vehicle or parts of vehicle components, the movement of the vehicle or parts or components of the vehicle, the forces acting on the vehicle or parts or components of the vehicle, the proximity of the vehicle to other vehicles or objects (stationary or moving), the yaw rate, the lateral slip angle, the steering angle, the overlap angle, the vehicle speed, the longitudinal acceleration and the lateral acceleration.

13. The method of claim 12, wherein determining whether a component anomaly exists comprises classifying the vibration pattern using a machine learning algorithm.

14. The method of claim 13, wherein the machine learning algorithm is trained with historical component anomaly data.

15. The system of claim 14, wherein classifying the vibration pattern using a machine learning algorithm further comprises: - generating a set of potential component anomalies based on the vibration pattern, - determining for each of the potential component anomalies a confidence score, and - selecting the component anomaly from the set of potential component anomalies based on the confidence scores.

16. The method of claim 6, further comprising: - assigning a weight to each of the set of potential component anomalies based on metadata for the potential component anomaly, and - select the component anomaly from the set of potential component anomalies based on the confidence score and weight.

17. The method of claim 12, further comprising: - receiving from the second sensor, located in a second position on the vehicle, additional sensor information produced by the detected vibration of the vehicle, and - determining the vibration pattern based on the sensor information and the additional sensor information.

18. The method of claim 12, further comprising: - before determining whether there is a component anomaly, determining whether the vibration pattern is recurring, and - determining whether there is a component anomaly in response to determining that the vibration pattern is recurring.

19. The method of claim 12, wherein performing the action to reduce comprises performing at least one of the actions selected from the group consisting of transmitting a notification to a vehicle owner, transmitting a notification to a fleet operator, transmitting a notification to a vehicle manufacturer, transmitting a notification to a public safety agency, commanding the vehicle to leave traffic, and generating an alert on the vehicle's human-machine interface.

20. The method of claim 12, wherein receiving the sensor information from the first sensor comprises receiving the sensor information from an accelerometer.

21. The method of claim 12, wherein determining the vehicle parameter comprises determining at least one parameter selected from the group consisting of: vehicle speed, wheel speed, steering angle, throttle level, braking level, gear selection and temperature.

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