CN116588008A

CN116588008A - Vibration-based component wear and failure automated detection for vehicles

Info

Publication number: CN116588008A
Application number: CN202310105872.3A
Authority: CN
Inventors: R·T·内斯比特; O·温德罗斯
Original assignee: Robert Bosch GmbH
Current assignee: Robert Bosch GmbH
Priority date: 2022-02-11
Filing date: 2023-02-13
Publication date: 2023-08-15

Abstract

Vibration-based component wear and failure automated detection for vehicles is provided. Systems and methods for detecting component anomalies for a vehicle using sensed vibrations. One example system includes a first sensor positioned at a first location on a vehicle and configured to sense vibrations of the vehicle, and an electronic processor communicatively coupled to the first sensor. The electronic processor is configured to receive sensor information generated from the sensed vehicle vibrations from the first sensor. The electronic processor is configured to determine a vibration mode based on the sensor information. The electronic processor is configured to determine whether a component anomaly exists based on the vibration pattern. The electronic processor is configured to perform a mitigation action based on the component exception in response to determining that the component exception exists.

Description

Vibration-based component wear and failure automated detection for vehicles

Cross Reference to Related Applications

The present application relates to and claims at 35U.S. c. ≡119 (e) the benefit from U.S. provisional patent application serial No. 63/309,139, entitled "Vibration Based Component Wear and Failure Detection for Vehicles," filed on 11 at 2.2.2, the entire contents of which are incorporated herein by reference.

Background

A vehicle operator may detect component anomalies or malfunctions of various vehicle subsystems including transmission, suspension, wheel balance, wheel alignment, brake rotors, wheel bearings, tie rods, exhaust, engine, and the like. These anomalies and malfunctions are observed by the driver based on noise, vibration, or harshness (NVH) feedback inside the cab. Some vehicles that are capable of autonomous driving may be used to provide taxi services, or may be used in carpooling applications. In both cases, there may be no conventional occupant or operator of the vehicle. In some examples, a fully autonomous taxi carries passengers without a human driver in the vehicle being present. In operation, such vehicles may experience component wear or failure.

Drawings

The accompanying figures, where like reference numerals refer to identical or functionally similar elements throughout the separate views and which together with the detailed description below are incorporated in and form part of the specification, serve to further illustrate embodiments of the concepts that include the claimed invention and to explain various principles and advantages of those embodiments.

FIG. 1 is a block diagram of a vehicle control system according to some examples.

Fig. 2 schematically illustrates an electronic controller of the system of fig. 1, according to some examples.

FIG. 3 is a flow chart of an example method for detecting component wear and failure according to some examples.

FIG. 4 is a block diagram of a vehicle control system according to some examples.

FIG. 5 is a flow chart of an example method for detecting component wear and failure according to some examples.

Skilled artisans will appreciate that elements in the figures are illustrated for simplicity and clarity and have not necessarily been drawn to scale. For example, the dimensions of some of the elements in the figures may be exaggerated relative to other elements to help improve the understanding of the embodiments of the present invention.

In the drawings, device and method components have been represented where appropriate by conventional symbols, showing only those specific details that are pertinent to understanding the embodiments of the present invention so as not to obscure the disclosure with details that will be readily apparent to those of ordinary skill in the art having the benefit of the description herein.

Detailed Description

When vehicles are operating on a roadway, they may experience component failure caused by ordinary wear, damage, or other conditions. As noted, a fully autonomous taxi may operate to transport passengers without a human driver in the vehicle being present. In addition, fully or partially autonomous vehicles may operate as part of a fleet or carpool plan. As a result, the occupant or driver may not use the same vehicle often enough to detect component anomalies or malfunctions. The vehicle is equipped with a sensor that detects a condition of the vehicle.

Although slight wear may not affect the technical functionality of the vehicle, it may still be desirable to treat the slight wear before it causes greater problems. Additionally, some noise, vibration, or harshness feedback may indicate a more serious or impending problem, which may render the vehicle inoperable. In the absence of a human driver present or willing to determine the cause of such feedback and take action on it, it is desirable for the autonomous vehicle to be able to do so automatically. Accordingly, provided herein are systems and methods for, among other things, automated component wear and failure detection, classification, and mitigation of vehicle systems, including autonomous driving systems.

Examples described herein provide systems that use vibration modes (e.g., sensing using an accelerometer or another type of vibration sensor) and other sensor inputs to detect component wear and failure. Using such an example, mitigation measures may be taken based on detected anomalies, if necessary. For example, depending on the nature of the component anomaly, the vehicle may notify its operator (e.g., fleet operator), contact the appropriate authorities, or both. Similarly, the vehicle may travel off the road, travel to an operations center for further investigation, or take other appropriate action based on component anomalies.

One example embodiment provides a system for detecting component anomalies for a vehicle. The system includes a first sensor positioned at a first location on the vehicle and configured to sense vibrations of the vehicle, and an electronic processor communicatively coupled to the first sensor. The electronic processor is configured to receive sensor information generated from the sensed vehicle vibrations from the first sensor. The electronic processor is configured to determine a vibration mode based on the sensor information. The electronic processor is configured to determine whether a component anomaly exists based on the vibration pattern. The electronic processor is configured to perform a mitigation action based on the component exception in response to determining that the component exception exists.

Another example embodiment provides a method for detecting component anomalies for a vehicle. The method includes receiving sensor information generated from sensed vehicle vibrations from a first sensor positioned at a first location on the vehicle. The method includes comparing, with an electronic processor communicatively coupled to the first sensor, the sensor information with a vibration noise substrate (floor) to extract one or more vibrations that exceed the vibration noise substrate. The method includes generating a vibration pattern based on one or more vibrations exceeding a vibration noise floor. The method includes determining whether a component anomaly exists based on the vibration pattern. The method includes, in response to determining that the component anomaly exists, performing a mitigation action based on the component anomaly.

As used herein, the term "component anomaly" refers to a component failure or a condition of a vehicle component, system, or subsystem that is outside of an acceptable range for the component, system, or subsystem. Examples of component anomalies include transmission anomalies (e.g., aged universal joints, low-level or malfunctioning torque converters), suspension anomalies (e.g., worn shock absorbers, ball joints, rocker arms, and control arm bushings), unbalanced wheels, improper wheel alignment, distorted brake rotors, malfunctioning wheel bearings, malfunctioning tie rods, exhaust system anomalies (e.g., leaking or malfunctioning mufflers), and engine anomalies (e.g., improperly installed or worn drive belts or worn motor housings).

Before any aspects of the invention are explained in detail, it is to be understood that the invention is not limited in its application to the details of construction and the arrangement of components set forth in the following description or illustrated in the following drawings. The invention is capable of being practiced or of being carried out in various ways.

It should also be noted that a plurality of hardware and software based devices, as well as a plurality of different structural components, may be used to implement the present invention. Moreover, it should be understood that the examples presented herein may include hardware, software, and electronic components or modules that, for purposes of discussion, may be illustrated and described as if the majority of the components were implemented solely in hardware. However, those skilled in the art will recognize, and based on a reading of this detailed description, that in at least one embodiment, the electronic-based aspects of the invention can be implemented in software (e.g., stored on a non-transitory computer-readable medium) executable by one or more processors. As such, it should be noted that the present invention may be implemented using a number of hardware and software based devices as well as a number of differently configured components. For example, the "control unit" and "controller" described in the specification may include one or more electronic processors, one or more physical memory modules including non-transitory computer readable media, one or more input/output interfaces, and various connections (e.g., a system bus) connecting the components.

For ease of description, some or all of the example systems presented herein are illustrated with a single paradigm for each component part thereof. Some examples may not describe or illustrate all components of a system. Other examples may include more or less of each illustrated component, may combine some components, or may include additional or alternative components.

FIG. 1 is a block diagram of one example of an autonomous vehicle control system 100. As described more particularly below, the autonomous vehicle control system 100 may be mounted on the vehicle 102 or integrated into the vehicle 102 and autonomously drive the vehicle. It should be noted that in the following description, the terms "autonomous vehicle" and "automated vehicle" should not be considered limiting. The term is used in a generic manner to refer to autonomous or automated driving vehicles that are provided with varying degrees of automation (i.e., the vehicle is configured to drive itself with limited, or in some cases no, input from the driver). The systems and methods described herein may be used with any vehicle capable of partially or fully autonomous operation, manual control by a driver, or some combination of the two. The term "driver" as used herein generally refers to a rider of an autonomous vehicle that sits in the position of the driver, operates a control of the vehicle while in manual mode, or provides control inputs to the vehicle to affect autonomous operation of the vehicle.

In the illustrated example, the system 100 includes an electronic controller 104, a vehicle control system 106, sensors 108, vibration sensors 110, a GNSS (global navigation satellite system) system 112, a transceiver 114, and a human-machine interface (HMI) 116. The components of system 100, along with other various modules and components, are electrically coupled to each other via or through one or more control or data buses (e.g., bus 118), which enable communication therebetween. In view of the invention described herein, one of ordinary skill in the art will recognize the use of control and data buses for interconnecting and communicating between the various modules and components. In some examples, bus 118 is a Control Area Network (CAN) ^TM ) A bus. In some examples, bus 118 is an automotive ethernet ^TM 、FlexRay ^TM A communication bus or another suitable wired bus. In alternative embodiments, some or all of the components of system 100 may use a suitable wireless modality (e.g., bluetooth ^TM Or near field communication) is communicatively coupled. For ease of description, the system 100 illustrated in FIG. 1 includes one for each of the foregoing components. Alternate embodiments may include one or more per componentOr some components may be excluded or combined.

The electronic controller 104 (described more particularly below with reference to fig. 2) operates the vehicle control system 106 and the sensors 108 to fully or partially autonomously control the vehicle, as described herein. The electronic controller 104 receives sensor telemetry from the sensors 108 and determines control data and commands for the vehicle. The electronic controller 104 transmits vehicle control data to the vehicle control system 106 to drive the vehicle (e.g., by generating braking signals, acceleration signals, steering signals), among other things.

The vehicle control system 106 includes controllers, sensors, actuators, etc. for controlling operational aspects of the vehicle 102 (e.g., steering, accelerating, braking, shifting, etc.). The vehicle control system 106 is configured to send and receive data related to the operation of the vehicle 102 to and from the electronic controller 104.

The sensors 108 determine one or more properties of the vehicle and its surroundings and communicate information about those properties to other components of the system 100 using, for example, electrical signals. Vehicle attributes include, for example, the position of the vehicle or a portion or component of the vehicle, the movement of the vehicle or a portion or component of the vehicle, the forces acting on the vehicle or a portion or component of the vehicle, the proximity of the vehicle to other vehicles or objects (stationary or moving), yaw rate, sideslip angle, steering wheel angle, overlay angle, vehicle speed, longitudinal and lateral acceleration, and the like. The sensors 108 may include, for example, vehicle control sensors (e.g., sensors that detect accelerator pedal position, brake pedal position, and steering wheel position [ steering angle ]), wheel speed sensors, vehicle speed sensors, yaw sensors, force sensors, range sensors, and vehicle proximity sensors (e.g., cameras, radar, LIDAR, and ultrasound). In some examples, the sensor 108 includes one or more cameras configured to capture one or more images of the environment surrounding the vehicle 102 according to their respective fields of view. The camera may include multiple types of imaging devices/sensors, each of which may be located at a different location inside or outside of the vehicle 102.

The vibration sensor 110 is a transducer capable of sensing vibrations in a vehicle component, converting the vibrations into an electrical signal, and transmitting the electrical signal to the electronic controller 104. In some examples, the vibration sensor 110 is an accelerometer. In some examples, the vibration sensor may be a strain gauge, an eddy current sensor, a gyroscope, a microphone, or another suitable vibration sensor. In some examples, the vibration sensor 110 may be integrated into another vehicle sensor (e.g., in combination with a wheel speed sensor of the vehicle 102). In some examples, multiple vibration sensors are used, for example mounted on each vehicle wheel, or at different points on the vehicle chassis. In some examples, the vibration sensor 110 is implemented using microelectromechanical system (MEMS) technology. As described herein, the electronic controller 104 processes the electrical signals received from the vibration sensor 110 to generate vibration patterns that can be analyzed to determine component anomalies that cause vibrations. In some examples, the vibration sensor 110 includes on-board signal processing circuitry that generates and transmits sensor information including vibration modes to the electronic controller 104 for processing.

The electronic controller 104 receives and interprets the signals received from the sensors 108 and vibration sensor 110 to automatically detect wear and failure of some vehicle components.

In some examples, the system 100 includes a GNSS (global navigation satellite system) system 112 in addition to the sensors 108. The GNSS system 112 receives radio frequency signals from the orbiting satellites using one or more antennas and receivers (not shown). The GNSS system 112 determines the geospatial positioning (i.e., latitude, longitude, altitude, and speed) of the vehicle based on the received radio frequency signals. The GNSS system 112 may communicate the positioning information to the electronic controller 104. When controlling the autonomous vehicle 102, the electronic controller 104 may use this information in conjunction with or in lieu of information received from some of the sensors 108.

Transceiver 114 comprises a radio transceiver that transmits data over one or more wireless communication networks (e.g., cellular network, satellite network, land mobile radio network, etc.) including communication network 120. The communication network 120 is a network including wireless connections, wiredA communication network of wired connections or a combination of both. The communication network 120 may be implemented using the following: wide area networks (e.g., the internet (including public and private IP networks)), long Term Evolution (LTE) networks, global system for mobile communications (or mobile expert Group (GSM)) networks, code Division Multiple Access (CDMA) networks, evolution data optimized (EV-DO) networks, enhanced data rates for global evolution (EDGE) networks, 3G networks, 4G networks, 5G networks, and one or more local area networks (e.g., bluetooth) ^TM Network or Wi-Fi network), and combinations or derivatives thereof.

The transceiver 114 also uses a suitable network modality (e.g., bluetooth ^TM Near Field Communication (NFC), wi-Fi ^TM Etc.) provides wireless communication within the vehicle. Accordingly, the transceiver 114 communicatively couples the electronic controller 104 and other components of the system 100 with networks or electronic devices both internal and external to the vehicle 102. For example, using transceiver 114, electronic controller 104 may communicate with fleet operators 122 of autonomous vehicles 102 to send and receive data, commands, and other information (e.g., component anomaly notifications). In another example, electronic controller 104 using transceiver 114 may contact an emergency authority (e.g., public Safety Answering Point (PSAP) 124) using an enhanced 911 (E911) communication modality. The transceiver 114 includes other components (e.g., amplifiers, antennas, baseband processors, etc.) that enable wireless communication, which are not described herein for the sake of brevity, and which may be implemented in hardware, software, or a combination of both. Some examples include multiple transceivers or separate transmission and reception components (e.g., transmitters and receivers), rather than a combined transceiver.

HMI 116 provides visual output such as, for example, a graphical indicator (i.e., a fixed or animated icon), light, color, text, image, a combination of the foregoing, and the like. HMI 116 includes suitable display mechanisms for displaying visual output, such as, for example, an instrument cluster, a mirror, a heads-up display, a center console display screen (e.g., a Liquid Crystal Display (LCD) touch screen or an Organic Light Emitting Diode (OLED) touch screen), or other suitable mechanism. In alternative embodiments, the display screen may not be a touch screen. In some examples, HMI 116 displays a Graphical User Interface (GUI) (e.g., generated by an electronic controller and presented on a display screen) that enables a driver or passenger to interact with autonomous vehicle 102. HMI 116 may also provide audible output to the driver, such as a chime, beep, voice output, or other suitable sound, through a speaker included in HMI 116 or separate from HMI 116. In some examples, HMI 116 provides haptic output to the driver by vibrating one or more vehicle components (e.g., a steering wheel and a seat of a vehicle), for example, using a vibration motor. In some examples, HMI 116 provides a combination of visual, audible, and tactile outputs.

In some examples, the electronic controller 104 communicates with the mobile electronic device 126 using the transceiver 114. In alternative embodiments, the mobile electronic device 126 may be communicatively coupled to the electronic controller 104 via a wired connection using, for example, a Universal Serial Bus (USB) connection or similar connection when proximate to the autonomous vehicle 102 or within the autonomous vehicle 102. The mobile electronic device 126 may be, for example, a smart phone, tablet computer, personal Digital Assistant (PDA), smart watch, or any other portable or wearable electronic device that includes or may be connected to a network modem or similar component (e.g., processor, memory, i/o interface, transceiver, antenna, etc.) that enables wireless or wired communication. In some examples, when mobile electronic device 126 is communicatively coupled to autonomous vehicle 102, HMI 116 communicates with mobile electronic device 126 to provide visual, audible, and tactile outputs through mobile electronic device 126.

Fig. 2 illustrates an example embodiment of an electronic controller 104, the electronic controller 104 including an electronic processor 205 (e.g., a microprocessor, an application specific integrated circuit, etc.), a memory 210, and an input/output interface 215. Memory 210 may be comprised of one or more non-transitory computer readable media and includes at least a program storage area and a data storage area. The program storage area and the data storage area may include a combination of different types of memory, such as read only memory ("ROM"), random access memory ("RAM") (e.g., dynamic RAM ("DRAM"), synchronous DRAM ("SDRAM"), etc.), electrically erasable programmable read only memory ("EEPROM"), flash memory, or other suitable memory devices. The electronic processor 205 is coupled to a memory 210 and an input/output interface 215. The electronic processor 205 sends information to, and receives information from, the memory 210 and/or the input/output interface 215, for example, and processes the information by executing one or more software instructions or modules that can be stored in the memory 210 or another non-transitory computer-readable medium. The software may include firmware, one or more applications, program data, filters, rules, one or more program modules, and other executable instructions. The electronic processor 205 is configured to retrieve and execute, among other things, software from the memory 210 for autonomous and semi-autonomous vehicle control and for performing the methods as described herein. In the illustrated embodiment, the memory 210 stores, among other things, a vibration detection algorithm 220, which vibration detection algorithm 220 operates as described herein to detect vibrations and classify vibration patterns to identify component anomalies.

Input/output interface 215 transmits and receives information from devices external to electronic controller 104 (e.g., components of system 100) via bus 118 (e.g., via one or more wired and/or wireless connections). Input/output interface 215 receives input (e.g., from sensor 108, HMI 116, etc.), provides system output (e.g., to HMI 116, etc.), or a combination of both. The input/output interface 215 may also include other input and output mechanisms, which are not described herein for the sake of brevity, and which may be implemented in hardware, software, or a combination of both.

In some examples, the electronic controller 104 analyzes the vibration data to identify component anomalies (as described herein) using one or more machine learning methods. Machine learning generally refers to the ability of a computer program to learn without explicit programming. In some examples, a computer program (e.g., a learning engine) is configured to construct an algorithm based on an input. Supervised learning involves presenting a computer program with example inputs and their desired outputs. The computer program is configured to learn from training data it receives a general rule mapping an input to an output. Example machine learning engines include decision tree learning, association rule learning, artificial neural networks, classifiers, inductive logic programming, support vector machines, clustering, bayesian networks, reinforcement learning, representation learning, similarity and metric learning, sparse dictionary learning, and genetic algorithms. Using these methods, a computer program can ingest, parse, and understand data, and gradually refine the algorithms used for data analysis.

It should be appreciated that while fig. 2 illustrates only a single electronic processor 205, memory 210, and input/output interface 215, alternative embodiments of electronic controller 104 may include multiple processors, memory modules, and/or input/output interfaces. It should also be noted that the system 100 may include other electronic controllers, each including similar components as the electronic controller 104, and configured similarly to the electronic controller 104. In some examples, the electronic controller 104 is partially or entirely implemented on a semiconductor (e.g., a field programmable gate array [ "FPGA" ] semiconductor) chip. Similarly, the various modules and controllers described herein may be implemented as individual controllers as illustrated, or as components of a single controller. In some examples, a combination of methods may be used.

FIG. 3 illustrates an example method 300 for automatically detecting, classifying, and/or mitigating a vehicle component anomaly. Although the method 300 is described in connection with the system 100 as described herein, the method 300 may be used with other systems and vehicles. Furthermore, the method 300 may be modified or performed differently than the specific examples provided. As an example, the method 300 is described as being performed by the electronic controller 104, and in particular the electronic processor 205. However, it should be understood that in some examples, portions of method 300 may be performed by other devices or subsystems of system 100.

At block 302, the electronic processor 205 receives sensor information from a first sensor (e.g., the vibration sensor 110) positioned at a first location on the vehicle and configured to sense vibrations of the vehicle. For example, the electronic processor 205 may receive signals from accelerometers positioned on wheels of the vehicle (e.g., via a CAN bus). In some examples, the electronic processor 205 continues to receive sensor information. In some examples, the electronic processor 205 receives periodic bursts of sensor information from the vibration sensor 110. In some examples, the sensor information is stored in a buffer or other memory of the electronic controller 104 until it can be processed.

At block 304, the electronic processor 205 determines a vibration mode based on the sensor information. In some examples, the vibration mode is determined by taking a sample of the sensor information. In some examples, the electronic processor 205 compares the sensor information to the vibration noise floor to extract one or more vibrations that exceed the vibration noise floor. In some examples, the vibration noise floor is a predetermined value set by the vehicle manufacturer. In some examples, the electronic processor 205 may establish a vibration noise floor as the vehicle operates over time. For example, the electronic processor 205 may periodically sample vibration information during normal vehicle operation and average the samples to establish a vibration noise floor. In some examples, the vibration noise floor value is adjusted based on current vehicle operating conditions. For example, the current speed and acceleration of the vehicle may be used to adjust the noise floor up or down to compensate for vibrations added by the operation of the vehicle. In some examples, the vibration noise floor is continuously determined using sensor information from other vibration sensors. For example, for a vehicle having one vibration sensor on each wheel, the electronic processor 205 may average the readings of all four sensors to determine the vibration noise floor. In another example, the electronic processor 205 may average readings of sensors that are not used to generate vibration modes to determine a vibration noise floor.

Regardless of how the vibration noise floor is determined, the electronic processor 205 may generate a vibration pattern based on one or more vibrations that exceed the vibration noise floor.

At block 306, the electronic processor 205 determines whether a component anomaly has occurred based on the vibration mode. For example, the electronic processor 205 may use a pattern matching algorithm to determine whether a vibration event matches a known vibration pattern associated with a particular component anomaly. In some examples, the electronic processor 205 may determine whether a component anomaly is present based on the vibration pattern and one or more vehicle attributes (e.g., received from one or more of the vehicle control system 106 or the sensors 108). For example, some types of vibrations may be more indicative of particular component failures when they occur during braking (e.g., a distorted rotor) or steering (e.g., a worn tie rod) maneuvers. For example, the electronic processor 205 may determine one or more vehicle attributes for a period of time that begins just before the vibration mode begins and ends just after the vibration mode ends (e.g., five seconds before and after the vibration mode occurs).

In some examples, the electronic processor 205 determines that component anomalies exist by classifying vibration patterns using a machine learning algorithm (e.g., a neural network or classifier) that is executable by the electronic processor 205. In some examples, machine learning algorithms are trained using historical component anomaly data. For example, the machine learning algorithm is fed with training data that includes example inputs (e.g., vibration pattern data representing anomalies of a particular component) and corresponding desired outputs (e.g., indications of component anomalies). The training data may also include metadata for vibration patterns. The metadata may include, for example, a vehicle speed at the vibration mode time, a vehicle model in which the vibration mode was sensed, a vehicle status (e.g., braking, accelerating, turning, etc.) at the vibration mode time, and an environmental condition (e.g., an environmental temperature, an environmental humidity, a weather condition, a road condition, etc.) at the vibration mode time. By processing the training data, the machine learning algorithm gradually develops a predictive model that maps inputs to outputs included in the training data.

In some examples, the vibration pattern is fed into a machine learning algorithm that identifies the cause of the component anomaly. In some examples, a machine learning algorithm generates a plurality of potential component anomalies based on vibration data and determines a confidence score for each potential component anomaly. The confidence score indicates how likely the potential component anomaly is that the cause of the vibration pattern (e.g., how closely the sensed vibration pattern matches the vibration pattern of the same type of potential component anomaly). In such an embodiment, the electronic processor 205 selects a component anomaly from a plurality of potential component anomalies based on the confidence score. For example, the potential component anomaly with the highest confidence score may be selected. In some examples, the confidence score is a numeric representation (e.g., from 0 to 1) of the confidence. For example, a vibration pattern may have a 60% match with one potential component anomaly, but may have an 80% match with another potential component anomaly, yielding confidence scores of 0.6 and 0.8, respectively.

Optionally, in some examples, the electronic processor 205 assigns weights to one or more potential component anomalies based on the vibration pattern and metadata of the potential component anomalies, and selects a component anomaly from the plurality of potential component anomalies based on the confidence score and weights.

The weights are used to indicate how important a particular piece of meta-data is to identify a potential component exception as a component exception relative to other potential component exceptions. For example, where both the vehicle experiencing the component anomaly and the vehicle generating training data for the potential component anomaly are of the same model, the potential component anomaly may be assigned a higher weight than if the metadata indicated two different vehicle models would be assigned. In another example, where a vehicle experiencing a component anomaly is accelerating and a vehicle generating training data for the potential component anomaly is decelerating, the potential component anomaly may be assigned a lower weight than if the metadata indicates that both vehicles are accelerating. Metadata with higher weights contributes more to the confidence score. For example, a smaller amount of higher weight metadata may yield a higher confidence score than a larger amount of lower weight metadata. In such embodiments, the electronic processor 205 determines a weighted confidence score for each of the plurality of potential component anomalies based on the confidence scores and the weights. For example, the electronic processor 205 may multiply the confidence score by the assigned weight. In such an embodiment, the electronic processor 205 selects a component anomaly from a plurality of potential component anomalies based on the weighted confidence score. For example, the component anomaly with the highest weighted confidence score may be selected.

In some examples, the weights are statically predetermined for each type of metadata. In some examples, the weights may be determined using a machine learning algorithm. Over time, when a match is determined for a vibration pattern and confirmed or rejected by observation, the machine learning algorithm may determine that particular metadata is more determinable as a high confidence score than other metadata and thus increase the weight of those metadata.

As illustrated in fig. 3, when the electronic processor 205 does not determine (at block 306) that a component anomaly has occurred (e.g., a vibration pattern does not match a known component anomaly), the electronic processor 205 continues to receive (at block 302) and process the sensor data to detect the component anomaly. In some examples, the electronic processor 205 is configured to continually detect and classify component anomalies. In other embodiments, electronic processor 205 is configured to periodically perform method 300 to detect component anomalies.

Regardless of how the component anomaly is determined, at block 308, the electronic processor 205 performs a mitigation action based on the component anomaly. In some examples, the mitigating action includes transmitting (e.g., via transceiver 114) a notification to a fleet operator. For example, a notification may be sent using an appropriate network message or API indicating that a component exception has occurred, the time and place of the component exception, the type of component exception, etc. In response to receiving the notification, the fleet operator may issue commands to the electronic controller 104 to drive the vehicle to the fleet facility for maintenance, drive the vehicle safely away from traffic (if needed) until another vehicle may be dispatched for the passenger(s), and so forth.

In some examples, the mitigating action includes transmitting (e.g., via transceiver 114) a notification to the public safety authority. For example, in the event that a potentially dangerous problem is causing a vibration mode, the electronic processor 205 may send an alert using the E911 system to relay information about the vehicle in distress and other information.

In some examples, the mitigating action includes controlling the vehicle to exit traffic. For example, in the event of an abnormally more severe component, the electronic controller 104 may autonomously operate the vehicle to travel off the road into a parking lot or other location relatively free of vehicle traffic. In some examples, the electronic controller 104 may autonomously operate the vehicle to travel to the maintenance facility.

In some examples, the mitigating action includes generating an alert on a human-machine interface of the vehicle to notify any passenger component anomalies and any other mitigating actions being taken. For example, a display of HMI116 may display a message such as "vehicle brakes need maintenance. We are going to the service facility for further evaluation of "or" vehicle wheel misalignment ". The vehicle operator is being alerted and the maximum vehicle speed will be reduced until the problem is resolved. "message. In some examples, HMI116 may speak an alert aloud to a vehicle occupant. In some examples, a combination of alarms may be used. In some examples, the electronic processor 205 may send an alert to the passenger's mobile electronic device (e.g., using the transceiver 114).

In some examples, multiple mitigation actions are combined.

Fig. 4 is a block diagram of one example of an autonomous vehicle control system 400. In system 400, electronic controller 104 receives sensor information from one or more accelerometers 110 and one or more vehicle condition inputs 402. As described herein, the electronic controller 104 uses sensor information and vehicle condition inputs to detect component anomalies and report the anomalies to various mitigation outputs 404 (using the transceiver 114), the HMI 116, or both.

FIG. 5 illustrates an example method 500 for automatically detecting, classifying, and/or mitigating a vehicle component anomaly. Although the method 500 is described in connection with systems 100 and 400 as described herein, the method 500 may be used with other systems and vehicles. Furthermore, the method 500 may be modified or performed differently than the specific examples provided. As an example, the method 500 is described as being performed by the electronic controller 104, and in particular the electronic processor 205. However, it should be understood that in some examples, portions of method 500 may be performed by other devices or subsystems of systems 100 and 400.

At block 502, the electronic processor 205 collects and compares accelerometer measurements to determine vibration modes, as described herein.

At block 504, the electronic processor 205 determines whether the vibration mode is a recurring mode (i.e., whether it 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 examples, the vibration pattern may have to exceed a threshold recurring value before the electronic processor 205 determines that the vibration pattern is a recurring vibration pattern. For example, in some instances, the vibration mode may have to occur three or more times to be considered to be repeated. At block 506, when the vibration pattern does not repeatedly occur, the electronic processor 205 stores the vibration pattern (e.g., in the memory 210) for comparison with future detected vibration patterns and ignores the vibration pattern (at block 508). In some examples, the electronic processor 205 continues to analyze the sensor information for vibration modes (at block 502) when the vibration modes are ignored.

At block 510, in response to determining that the vibration pattern is a recurring vibration pattern, the electronic processor 205 determines whether the vibration event is associated with a previously stored vibration event. As used herein, the term "vibration event" refers to a detected recurring vibration pattern that incorporates metadata associated with the vibration pattern. In some examples, the metadata includes current vehicle system data within a time frame that includes a time during which the recurring vibration pattern was sensed. The vehicle system data may include values of the vehicle condition inputs 402, condition values or commands from the vehicle system 106, inputs from the sensors 108, or a combination of the foregoing. In some examples, the electronic processor 205 determines whether the vibration event is related to a previously stored vibration event by utilizing similar techniques as described herein with respect to the method 300 and determining whether a component anomaly exists.

In some examples, the electronic processor combines the functions of blocks 504 and 510 to check for recurring vibration events, rather than first checking for recurring vibration modes. For example, each time a vibration pattern is detected, the metadata is combined to create a vibration event, which is then checked for repeated occurrences before proceeding to block 516.

When the vibration event is not correlated to a previously stored vibration event at block 512, the electronic processor 205 stores the vibration event as a new event (e.g., in the memory 210) and ignores the vibration event (at block 514). In some examples, when the vibration event is ignored, the electronic processor 205 continues to analyze the sensor information for vibration patterns and possible vibration events (at block 502).

At block 516, when the vibration event does correlate to a previously stored vibration event, the electronic processor 205 determines whether the vibration event is specific to one sensor (i.e., the vibration mode is detected at only one of the plurality of vibration sensors). For example, the electronic processor 205 compares data from multiple accelerometers 110 to determine whether a vibration mode including a vibration event is detected at only one accelerometer 110 or at more than one accelerometer 110. At block 518, when the vibration event is specific to one of the sensors, the electronic processor 205 correlates the vibration pattern to a wheel-specific anomaly (e.g., associated with the wheel where one of the sensors is located). At block 520, the electronic processor 205 determines whether the vibration pattern matches a particular type of component anomaly (as described herein). At block 522, if it does not match, the vibration event is ignored. In some examples, when the vibration event is ignored, the electronic processor 205 continues to analyze the sensor information for vibration patterns and possible vibration events (at block 502). At block 524, if it does match, the exception is recorded and a mitigating action is taken (e.g., by sending an alarm).

At block 526, the electronic processor 205 correlates the source of the vibration mode to the vehicle chassis (e.g., alignment, transmission, engine, exhaust, etc.) when the event is not specific to one sensor (i.e., the vibration mode is sensed at more than one of the number of vibration sensors). At block 528, the electronic processor 205 determines whether the vibration pattern matches a particular type of component anomaly (as described herein). At block 530, if it does not match, the event is recorded and an alert is sent regarding the potential problem that the vehicle is unknown or unspecified. At block 524, if it does match, the exception is recorded and a mitigating action is taken (e.g., by sending an alarm).

Accordingly, among other things, embodiments described herein provide a control system for an autonomous vehicle configured to detect and mitigate component anomalies.

In the foregoing specification, specific embodiments have been described. However, those of ordinary skill in the art will appreciate that various modifications and changes can be made without departing from the scope of the present invention as set forth in the claims below. Accordingly, the specification and figures are to be regarded in an illustrative rather than a restrictive sense, and all such modifications are intended to be included within the scope of present teachings.

Also, the phraseology and terminology used herein is for the purpose of description and should not be regarded as limiting. In this document, relational terms such as first and second, top and bottom, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. The terms "comprises," "comprising," "having," "including," "includes," "including," "containing," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises, has, includes, contains a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Elements beginning with "comprising … … one," "having … … one," "including … … one," or "containing … … one" do not exclude the presence of additional identical elements in a process, method, article, or apparatus that comprises, has, includes, contains the element without further constraints. The terms "a" and "an" are defined as one or more unless explicitly stated otherwise herein. The terms "substantially," "essentially," "approximately," "about," or any other version thereof are defined as being close to those of ordinary skill in the art understand, and in one non-limiting embodiment, the term is defined to be within 10%, in another embodiment within 5%, in another embodiment within 1%, and in another embodiment within 0.5%. The terms "connected" and "coupled" are used broadly and encompass both direct and indirect connections and couplings. Furthermore, "connected" and "coupled" are not restricted to physical or mechanical connections or couplings, and may include direct or indirect electrical connections or couplings. Further, electronic communications and notifications may be performed using wired connections, wireless connections, or combinations thereof, and may be transmitted directly or through one or more intermediary devices through various types of networks, communication channels, and connections. A device or structure that is "configured" in a particular manner is configured in at least that manner, but may also be configured in ways that are not listed.

Various features, advantages and embodiments are set forth in the following claims.

Claims

1. A system for detecting component anomalies for a vehicle, the system comprising:

a first sensor positioned at a first location on the vehicle and configured to sense vibrations of the vehicle; and

an electronic processor communicatively coupled to the first sensor and configured to receive sensor information generated by sensed vehicle vibrations from the first sensor;

determining a vibration mode based on the sensor information;

determining whether a component anomaly exists based on the vibration pattern; and

in response to determining that the component anomaly exists, a mitigation action is performed based on the component anomaly.

2. The system of claim 1, wherein the electronic processor is configured to determine the vibration mode by:

comparing the sensor information to a vibration noise floor to extract one or more vibrations exceeding the vibration noise floor; and

a vibration pattern is generated based on the one or more vibrations exceeding the vibration noise floor.

3. The system of claim 1, wherein the electronic processor is further configured to:

determining a vehicle attribute; and

whether a component anomaly exists is determined based on the vibration pattern and the vehicle attribute.

4. The system of claim 1, wherein the electronic processor is configured to determine whether a component anomaly is present by classifying the vibration pattern using a machine learning algorithm.

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

6. The system of claim 5, wherein the electronic processor is further configured to classify the vibration pattern using a machine learning algorithm by

Generating a plurality of potential component anomalies based on the vibration pattern;

determining a confidence score for each potential component anomaly; and

component anomalies are selected from the plurality of potential component anomalies based on the confidence scores.

7. The system of claim 6, wherein the electronic processor is further configured to:

assigning a weight to each of the plurality of potential component anomalies based on metadata of the potential component anomaly; and

component anomalies are selected from the plurality of potential component anomalies based on the confidence scores and weights.

8. The system of claim 1, further comprising:

a second sensor positioned at a second location on the vehicle and configured to sense vibrations of the vehicle, wherein the electronic processor is communicatively coupled to the second sensor and further configured to

Receiving additional sensor information generated by the sensed vehicle vibration from the second sensor; and

the vibration mode is determined based on the sensor information and the additional sensor information.

9. The system of claim 1, wherein the electronic processor is further configured to:

determining whether the vibration pattern repeatedly occurs before determining whether the component abnormality exists; and

in response to determining that the vibration pattern is recurring, determining whether a component anomaly exists.

10. The system of claim 1, wherein the mitigation action is at least one 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, controlling vehicle exit from traffic, and generating an alert on a human-machine interface of the vehicle.

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

12. The system of claim 3, wherein the vehicle attribute is at least one selected from the group consisting of: vehicle speed, wheel speed, steering angle, throttle level, brake level, gear selection, and temperature.

13. A method for detecting component anomalies for a vehicle, the method comprising:

receiving sensor information generated from sensed vehicle vibrations from a first sensor positioned at a first location on the vehicle;

comparing, with an electronic processor communicatively coupled to the first sensor, the sensor information to the vibration noise substrate to extract one or more vibrations exceeding the vibration noise substrate;

generating a vibration pattern based on one or more vibrations exceeding the vibration noise floor;

14. The method of claim 13, further comprising:

determining a vehicle attribute; and

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

16. The method of claim 15, wherein the machine learning algorithm is trained on historical component anomaly data.

17. The system of claim 16, wherein classifying vibration modes using a machine learning algorithm further comprises:

determining a confidence score for each potential component anomaly; and

18. The method of claim 17, further comprising:

19. The method of claim 13, further comprising:

receiving additional sensor information generated from sensed vehicle vibrations from a second sensor positioned at a second location on the vehicle; and

20. The method of claim 13, further comprising:

21. The method of claim 13, wherein performing a mitigation action comprises performing at least one 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, controlling vehicle exit from traffic, and generating an alert on a human-machine interface of the vehicle.

22. The method of claim 13, wherein receiving sensor information from a first sensor comprises receiving sensor information from an accelerometer.

23. The method of claim 14, wherein determining a vehicle attribute comprises determining at least one selected from the group consisting of: vehicle speed, wheel speed, steering angle, throttle level, brake level, gear selection, and temperature.