Vehicle fault detection method and device and vehicle

Abstract

The invention discloses a vehicle fault diagnosis method and device and a vehicle, and aims to detect common cause failure of a wheel speed signal and ensure the safety of the vehicle in a fault state. Comprising the following steps: acquiring a sensing signal of a vehicle dynamic sensor assembly, generating dynamic data of a vehicle, and performing consistency processing on the dynamic data to calculate an acceleration deviation value; whether the acceleration deviation value exceeds a preset fault threshold value or not is judged, and if yes, the exceeding frequency is recorded; and in a preset diagnosis time period, if the number of times exceeding the fault threshold value is larger than a preset number-of-times threshold value, a power system of the vehicle is triggered to degrade, and an alarm is given out. According to the invention, the potential fault of the vehicle power system can be timely and accurately detected, the potential safety hazard caused by the fault is reduced, and the reliability and safety of vehicle operation are improved. According to the invention, the abnormity of the wheel speed signal under the common cause failure condition can be effectively detected, degradation and alarm measures are taken in time, and the driving safety of the vehicle is guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of automobile technology, and in particular to a vehicle fault detection method, device and vehicle. Background Art

[0002] In the prior art, the wheel speed signal of a vehicle is a key data source for the normal operation of the vehicle dynamic control system (such as the anti-lock braking system ABS, the traction control system TCS and the vehicle dynamic control system VDC). These systems detect the changes in the wheel speed of each wheel to control the stability and traction of the vehicle. However, since a single wheel speed sensor can only meet a lower functional safety level (such as ASIL B), the common design requires mutual verification of four wheel speed signals so that the wheel speed signals can meet higher safety requirements (such as ASIL D).

[0003] Existing methods rely on the consistency of data from multiple wheel speed sensors, but this method has a significant flaw: when the four wheel speed sensors fail consistently due to the same reason (common cause failure), it is difficult for the system to effectively detect the anomaly, causing the vehicle dynamic control system to fail to work properly. In this case, the stability and safety of the vehicle may be seriously affected, especially in high-risk driving scenarios, which may cause the driver to lose control of the vehicle.

[0004] Therefore, an improved fault diagnosis method is needed that can detect the anomaly in a timely manner when a common cause failure occurs in the wheel speed sensor, and use other vehicle dynamic data to assist in stability control to ensure that the vehicle can still maintain basic stability and safety when the wheel speed signal fails. Summary of the invention

[0005] The purpose of the present application is to provide a vehicle fault diagnosis method, device and vehicle, which can solve the problem of common cause failure of wheel speed sensors that is difficult to detect in the prior art, so as to improve the accuracy of fault detection and ensure the stability and safety of the vehicle when the wheel speed signal is abnormal.

[0006] To achieve the above objectives: In a first aspect, an embodiment of the present application provides a method, the method comprising: S1: acquiring a sensing signal of a vehicle dynamic sensor assembly, generating dynamic data of the vehicle according to the sensing signal, and performing consistency processing on the dynamic data to calculate an acceleration deviation value of the vehicle; S2: Determine whether the acceleration deviation value exceeds a preset fault threshold, and if so, record the number of times the acceleration deviation value exceeds the fault threshold; S3: within a preset diagnosis period, if the acceleration deviation value exceeds the fault threshold for a number of times greater than a preset number threshold, the vehicle's power system is triggered to degrade and an alarm is issued.

[0007] In one embodiment, the step S1 further includes: acquiring the driving state of the vehicle, and selecting a target sensor of the vehicle dynamic sensor assembly according to the driving state of the vehicle, wherein the target sensor includes at least two sensors in the vehicle dynamic sensor assembly.

[0008] In one embodiment, the fault threshold and the preset diagnosis period are dynamically adjusted according to the driving state of the vehicle and the type of the target sensor; The driving status includes straight driving, acceleration, deceleration and turning status.

[0009] In one embodiment, performing validity detection on the acquired dynamic data includes: Check whether the acquired sensor data of the same type is within its normal working range; The normal data are weighted averaged, and based on the weighted average result, the data are converted into the same unit for consistency processing.

[0010] In one embodiment, the target sensors are wheel speed sensors and longitudinal acceleration sensors; The step S1 comprises: acquiring a sensing signal of a wheel speed sensor, and generating wheel speed data of the vehicle according to the sensing signal; Acquiring a sensing signal from a longitudinal acceleration sensor, and generating longitudinal acceleration data of the vehicle according to the sensing signal; The consistency processing is: converting the wheel speed data into acceleration data.

[0011] In one embodiment, the calculation of the acceleration deviation value includes: calculating the difference between the converted acceleration data and the longitudinal acceleration data to detect the degree of the fault or whether the fault threshold is met.

[0012] In one embodiment, the system degradation includes the following measures: Turn off the vehicle's anti-lock braking system (ABS), traction control system (TCS) and vehicle dynamic control system (VDC); Limit the vehicle's maximum speed and acceleration to ensure safety in the event of a malfunction; Provide fault alerts to the driver to indicate possible sensor failure or system failure; The fault information is sent to the vehicle's central control bus (CAN) for recording and facilitating subsequent maintenance. In one embodiment, the fault diagnosis method includes, upon detecting a common cause failure of a wheel speed sensor, initiating an alternative control strategy based on other dynamic data of the vehicle: The vehicle dynamics control system is adjusted based on the vehicle's steering wheel angle, yaw rate and other data to ensure minimal vehicle stability control.

[0013] In a second aspect, an embodiment of the present application provides a vehicle fault diagnosis device, comprising: Data acquisition and processing module: electrically connected to the dynamic touch sensor assembly; Fault diagnosis and response module: completes fault diagnosis based on the data collected by the data acquisition and processing module, and triggers the response mechanism when a fault is detected; The dynamic sensor assembly includes a wheel speed sensor, an acceleration sensor, an inertial measurement unit, a steering wheel angle sensor and a gyroscope.

[0014] In a third aspect, an embodiment of the present application provides a vehicle, which uses the above-mentioned vehicle fault diagnosis device. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A schematic diagram of a vehicle fault diagnosis method according to an embodiment of the present invention; Figure 2 A specific flow chart of a vehicle fault diagnosis method is provided for an embodiment of the present invention. DETAILED DESCRIPTION

[0016] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0017] It should be noted that, in this article, the terms "include", "comprises" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element. In addition, components, features, and elements with the same name in different embodiments of the present application may have the same meaning or different meanings, and their specific meanings need to be determined by their explanation in the specific embodiment or further combined with the context of the specific embodiment.

[0018] It should be understood that, although the terms first, second, third, etc. may be used to describe various information in this article, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of this article, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein can be interpreted as "at the time of" or "when" or "in response to determination". Furthermore, as used in this article, the singular forms "one", "one" and "the" are intended to also include plural forms, unless there is an opposite indication in the context. It should be further understood that the terms "comprising", "including" indicate that there are described features, steps, operations, elements, components, projects, kinds, and / or groups, but do not exclude the existence, occurrence or addition of one or more other features, steps, operations, elements, components, projects, kinds, and / or groups. The terms "or" and "and / or" used herein are interpreted as inclusive, or mean any one or any combination. Thus, “A, B, or C” or “A, B and / or C” means “any of the following: A; B; C; A and B; A and C; B and C; A, B, and C.” An exception to this definition will occur only when a combination of elements, functions, steps, or operations are inherently mutually exclusive in some manner.

[0019] It should be understood that, although the various steps in the flowchart in the embodiment of the present application are displayed in sequence according to the indication of the arrows, these steps are not necessarily performed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and it can be performed in other orders. Moreover, at least a portion of the steps in the figure may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and their execution order is not necessarily performed in sequence, but can be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0020] It should be noted that, in this article, step codes such as S101, S102, etc. are used for the purpose of expressing the corresponding content more clearly and concisely, and do not constitute a substantial limitation on the sequence. When implementing the step, those skilled in the art may execute S102 first and then S101, etc., but these should all be within the scope of protection of this application.

[0021] It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0022] In the subsequent description, the suffixes such as "module", "component" or "unit" used to represent elements are only used to facilitate the description of the present application and have no specific meaning. Therefore, "module", "component" or "unit" can be used in a mixed manner.

[0023] The following is an explanation of the terms that may be involved in this application, as follows: Common Cause Failure (CCF): refers to the phenomenon that multiple components or subsystems in a system fail at the same time due to the same root cause. The root cause of common cause failure is usually external factors (such as temperature, vibration, electromagnetic interference) or internal defects (such as design defects, material defects, etc.), which cause multiple components in the system that should operate independently to fail in a consistent manner.

[0024] NBC (Network-Based Control): refers to a network-based control system, which is mainly used to achieve remote control and coordination of equipment or subsystems in a distributed system. This control method connects different control nodes or sensors through the network, allowing the control system to collect data, transmit instructions, monitor status in real time, and make dynamic adjustments based on feedback data.

[0025] See also Figure 1, is a vehicle fault diagnosis method provided in an embodiment of the present application, which detects the common cause failure of the wheel speed signal by comparing and analyzing the data of the wheel speed sensor and the second sensor, thereby ensuring the driving safety of the vehicle. The method provided in this embodiment includes: Step S1, acquiring a sensing signal of a vehicle dynamic sensor assembly, generating dynamic data of the vehicle according to the sensing signal, and performing consistency processing on the dynamic data to calculate an acceleration deviation value of the vehicle.

[0026] The vehicle is equipped with a dynamic sensor assembly, including wheel speed sensors, acceleration sensors, and inertial measurement units (IMUs). Through these sensors, the system can obtain dynamic signals of the vehicle in real time. For example, the wheel speed sensor collects wheel speed information, the acceleration sensor provides longitudinal acceleration values, and the IMU outputs multi-axis dynamic signals such as angular velocity and linear acceleration. The system preprocesses these signals, including filtering and denoising, to ensure data accuracy. Subsequently, the vehicle's dynamic data, such as wheel speed, acceleration values, and posture change data, are generated based on the preprocessed signals. The system further performs consistency processing on these dynamic data, unifies the data units and eliminates possible deviations, and finally calculates the acceleration deviation value of the vehicle to measure the degree of abnormality of the current dynamic state.

[0027] Step S2: determine whether the acceleration deviation value exceeds a preset fault threshold, and if so, record the number of times the acceleration deviation value exceeds the fault threshold.

[0028] After calculating the acceleration deviation value, the system compares it with the preset fault threshold. The threshold is set based on historical data and safety requirements under normal vehicle driving conditions. If the acceleration deviation value exceeds the threshold, it is considered that the current vehicle has a dynamic abnormality, and the system will record the number of occurrences of this abnormal event. The recorded number of times will serve as the basis for subsequent fault diagnosis. If the acceleration deviation value does not exceed the threshold, the system continues to monitor dynamic data and does not record abnormalities.

[0029] Step S3: within a preset diagnosis period, if the acceleration deviation value exceeds the fault threshold for a number of times greater than a preset number threshold, the vehicle's power system is triggered to degrade and an alarm is issued.

[0030] The system sets a diagnostic period, such as 10 seconds as a time window. During this period, the system counts the cumulative number of times the acceleration deviation value exceeds the fault threshold. If the statistical results show that the number of times the threshold is exceeded exceeds the preset number threshold (for example, 5 times), it is determined that the vehicle power system may have a fault. At this time, the system will trigger the power system degradation function to reduce the impact of the fault on vehicle safety by limiting power output. At the same time, the system sends an alarm signal to remind the driver that the vehicle has a fault. The alarm can be in the form of lighting up the warning light on the dashboard or emitting a sound prompt.

[0031] Through the above steps, the present invention can effectively detect potential faults in the vehicle power system and take response measures in a timely manner when conditions are met to ensure the safety and reliability of vehicle operation.

[0032] In one embodiment, the driving state of the vehicle is acquired, and a target sensor of the vehicle dynamic sensor assembly is selected according to the driving state of the vehicle, so as to further improve the accuracy and adaptability of fault detection.

[0033] First, the system obtains the current driving status information through the vehicle's control module, such as whether the vehicle is in a straight-line, accelerating, decelerating or turning state. The driving status is monitored in real time by on-board sensors (such as steering wheel angle sensor, longitudinal acceleration sensor and vehicle speed sensor) and output to the diagnostic system.

[0034] After the driving state is determined, the system selects the most suitable target sensor according to the current state to obtain dynamic signal data. For example, when the vehicle is in a straight-line driving state, the system gives priority to the data of the longitudinal acceleration sensor and wheel speed sensor, because the data of these sensors can better reflect the dynamic stability of the vehicle; when the vehicle is in a turning state, the system gives priority to the lateral acceleration sensor and steering wheel angle sensor, so as to more accurately capture the dynamic characteristic changes caused by turning.

[0035] Through this method of selecting target sensors based on driving status, the diagnostic system can avoid blindly processing all sensor data, reduce the complexity of data processing, and improve the accuracy of acceleration deviation value calculation. Subsequently, the system performs consistency processing on the data of the selected target sensor and calculates the acceleration deviation value of the vehicle, providing an accurate data basis for subsequent fault diagnosis.

[0036] Through this method, this embodiment can flexibly select the target sensor according to the actual driving state of the vehicle, thereby improving the efficiency of the diagnosis system and the reliability of the diagnosis result.

[0037] Based on the same inventive concept as the above embodiments, the above embodiments are described in detail below through a specific application scenario. When the vehicle is in a straight-ahead state, the wheel speed sensor and the longitudinal acceleration sensor are selected because the longitudinal acceleration can effectively reflect the acceleration or deceleration of the vehicle in a straight-ahead state and has a high correlation with the data of the wheel speed sensor.

[0038] When the vehicle is in an acceleration or deceleration state, the wheel speed sensor and longitudinal acceleration sensor are also selected, because the longitudinal acceleration of the vehicle will directly reflect the movement changes of the vehicle during the acceleration or deceleration process.

[0039] When the vehicle is turning, select the wheel speed sensor and lateral acceleration sensor. The lateral acceleration sensor can reflect the change of lateral acceleration during the turning process, while the speed sensor provides the overall speed data of the vehicle when turning, which is consistent with the value of the wheel speed sensor.

[0040] Through this driving state perception and target sensor selection approach, the method can dynamically adjust the detection mode under different driving states, thereby improving the flexibility and accuracy of fault diagnosis and effectively assisting the identification of common cause failures.

[0041] In a specific implementation, the threshold setting and diagnosis period selection in vehicle fault diagnosis are further optimized, and the diagnosis strategy can be dynamically adjusted according to the vehicle's driving status and the type of target sensor, thereby improving the accuracy and adaptability of fault diagnosis.

[0042] The fault threshold and the preset diagnosis period are dynamically adjusted according to the driving state of the vehicle and the type of the target sensor. The driving state includes straight driving, acceleration, deceleration and turning state.

[0043] In this embodiment, the current driving state is first obtained through the vehicle control module. The vehicle driving state includes but is not limited to straight driving, acceleration, deceleration and turning. The system determines the specific driving state of the current vehicle by collecting data from the steering wheel angle sensor, acceleration sensor and vehicle speed sensor. For example: When the steering wheel angle is close to zero, the longitudinal acceleration is stable and the vehicle speed is relatively constant, it is judged to be in a straight-ahead state; When the longitudinal acceleration is positive and gradually increases, it is judged to be in an accelerating state; When the longitudinal acceleration is negative, it is judged to be in a deceleration state; When the steering wheel angle deviates from zero and the lateral acceleration signal is significant, it is judged to be a turning state.

[0044] Based on the judgment results of the driving status, the system dynamically adjusts the fault threshold and diagnosis period: For example: When the vehicle is in a straight-ahead state, the fault threshold is set to a lower value, such as an acceleration deviation of ±0.2 m / s². The diagnostic period can be set to a shorter period (such as 5 seconds) because the dynamic changes in the straight-ahead state are relatively small and it is easy to detect faults.

[0045] When the vehicle is in the acceleration state, the fault threshold is appropriately relaxed, for example, set to an acceleration deviation of ±0.5 m / s² to adapt to the normal fluctuations of vehicle power changes. The diagnostic period is set to a medium length (such as 10 seconds) to capture the dynamic characteristics during acceleration.

[0046] When the vehicle is in a deceleration state, the fault threshold is similar to that in an acceleration state, set to an acceleration deviation of ±0.5 m / s². The diagnostic period can be dynamically adjusted according to the time the vehicle decelerates, usually 5-10 seconds.

[0047] When the vehicle is in a turning state, the fault threshold needs to be adjusted according to the lateral acceleration, for example, ±0.8 m / s². The diagnostic period is appropriately extended to 15 seconds to ensure that the stability and consistency of the vehicle in turning can be monitored.

[0048] In addition, different target sensor types will also affect the setting of fault thresholds and diagnostic periods. For example: When the target sensor is a wheel speed sensor, the fault threshold is adjusted mainly based on the vehicle's driving state and wheel slip rate. In straight driving, the slip rate threshold can be set to 5%; in turning state, it can be appropriately relaxed to 10%.

[0049] When the target sensor is an acceleration sensor, the threshold adjustment is based on the direction and magnitude of the acceleration, focusing on monitoring the abnormalities of longitudinal and lateral dynamic changes.

[0050] When the target sensor is an IMU, the diagnostic strategy will incorporate comprehensive analysis of multi-axis data, such as the changing trends of acceleration and angular velocity, to more comprehensively capture the dynamic characteristics of the vehicle.

[0051] By dynamically adjusting the fault threshold and diagnostic period, the diagnostic method of the present invention can more accurately adapt to different driving conditions and sensor characteristics, thereby improving the sensitivity and adaptability of fault detection. At the same time, the method avoids the problem of misjudgment or missed judgment that may be caused by fixed thresholds or diagnostic periods.

[0052] In a specific implementation, the acquired dynamic data is tested for validity to ensure the accuracy of the data, thus laying a foundation for consistency comparison.

[0053] Check whether the acquired sensor data of the same type is within its normal working range; The normal data are weighted averaged, and based on the weighted average result, the data are converted into the same unit for consistency processing.

[0054] For example: Convert the data (rotation speed) of the wheel speed sensor into linear speed (m / s) through the wheel radius; The acceleration data output by the IMU is standardized with the output of the acceleration sensor so that they have the same reference coordinate system and unit.

[0055] The system compares the converted data with the weighted average result for consistency.

[0056] For example: Compare the vehicle speed calculated by the wheel speed sensor with the linear speed measured by the IMU to see if they are consistent; Compare the longitudinal acceleration data provided by the longitudinal acceleration sensor and the IMU to see if they are within a reasonable range.

[0057] If the deviation between the data is within the allowable range (such as ±5%), the data are judged to be consistent, otherwise the abnormality is marked and recorded for subsequent diagnosis.

[0058] Through the above-mentioned validity detection method, this embodiment can eliminate abnormal data, enhance the representativeness of the remaining data, and ensure the reliability and comparability of different sensor data through conversion and consistency processing, providing an accurate data basis for subsequent acceleration deviation calculation and fault diagnosis.

[0059] See also Figure 2 In a specific implementation, a sensing signal of a wheel speed sensor is obtained, and wheel speed data of the vehicle is generated according to the sensing signal; a sensing signal of a longitudinal acceleration sensor is obtained, and longitudinal acceleration data of the vehicle is generated according to the sensing signal; the consistency processing is: converting the wheel speed data into acceleration data.

[0060] In this embodiment, the sensing signal is first collected from the wheel speed sensor of the vehicle. The wheel speed sensor is installed on each wheel and generates a pulse signal proportional to the wheel speed by detecting the rotation of the ring gear. The system samples and digitizes these signals to generate wheel speed data (unit: revolutions per minute) for each wheel. Subsequently, the system merges the wheel speed data of each wheel into the overall wheel speed data of the vehicle through a weighted average method and converts it into a linear speed (unit: m / s) for subsequent processing.

[0061] At the same time, the system collects sensing signals from the vehicle's longitudinal acceleration sensor. This sensor directly detects the acceleration of the vehicle in the forward direction and outputs dynamic data in m / s². The system filters and denoises the data collected by the acceleration sensor to ensure its accuracy and stability.

[0062] For consistency processing, the system converts wheel speed data into acceleration data. Based on the physical formula The system uses wheel speed data to calculate the equivalent acceleration of the vehicle. During the calculation process, the system uses a fixed sampling time interval and wheel speed change Through this conversion, the wheel speed sensor data is converted into a form that can be directly compared with the acceleration sensor data.

[0063] In the consistency processing stage, the system compares the converted wheel speed acceleration data with the longitudinal acceleration data of the acceleration sensor. If the difference between the two is within the preset threshold range (such as 0.5 m / s²), the data is considered consistent, indicating that the sensor is working properly; if the difference exceeds the threshold, the system determines it as abnormal data and records the abnormal situation for subsequent diagnosis and analysis.

[0064] This embodiment can timely detect possible abnormalities of a single sensor by checking the consistency of the wheel speed sensor and the longitudinal acceleration sensor data, thus avoiding misjudgment caused by single-point failure. At the same time, the method also enhances the accuracy of vehicle dynamic data processing, providing a reliable basis for subsequent fault detection and response.

[0065] In a specific implementation, the calculation of the acceleration deviation value includes: calculating the difference between the converted acceleration data and the longitudinal acceleration data to detect the degree of the fault or whether the fault threshold is met. In vehicle fault diagnosis, the system first converts the wheel speed value of the wheel speed sensor into an acceleration value to make the data consistent with the unit of the longitudinal acceleration sensor. Then, the system calculates the difference between the converted wheel speed acceleration value and the longitudinal acceleration value to determine the absolute deviation between the two.

[0066] When the difference is less than the preset fault threshold, the system considers that the data of the wheel speed sensor and the longitudinal acceleration sensor are basically consistent, indicating that the vehicle movement is normal; if the difference exceeds the fault threshold, the system records the abnormal situation and considers that the wheel speed signal is inconsistent with the actual movement state of the vehicle, and there may be a risk of sensor failure or common cause failure.

[0067] The difference calculation is mainly applicable to low-speed driving or low vehicle acceleration. In this state, the absolute deviation between wheel speed data and longitudinal acceleration data is usually small, so the difference judgment can accurately capture the subtle differences between sensors and is suitable for slow acceleration or constant speed driving scenarios.

[0068] In a specific implementation, the system can also detect the proportional relationship between the wheel speed acceleration value and the longitudinal acceleration value by calculating the quotient between the two to evaluate their relative consistency. The system first obtains wheel speed data from the wheel speed sensor and converts it into an acceleration value, and then calculates the quotient of the converted wheel speed acceleration value and the longitudinal acceleration value.

[0069] When the quotient is within the threshold range set by the system, the system considers the data to be matched, indicating that the vehicle's wheel speed signal and longitudinal acceleration signal are consistent; when the quotient exceeds the allowable range, it indicates that the proportional relationship between the wheel speed sensor data and the longitudinal acceleration data is abnormal, and there may be a risk of failure. The quotient detection can ignore fluctuations under high numerical conditions by focusing on the proportional difference between the two rather than the absolute difference.

[0070] The applicable scenarios of quotient calculation are mainly when driving at high speed or when the vehicle acceleration changes greatly. In this case, the absolute values ​​of wheel speed and acceleration are large, and the difference may fluctuate greatly, affecting the accuracy. The quotient judgment is suitable for detecting fault signals of vehicles at high speed or violent acceleration through relative proportion analysis to avoid misjudgment.

[0071] Combining difference and quotient calculations can improve the accuracy and flexibility of fault diagnosis. Generally, at low speeds or steady acceleration, difference judgment can effectively detect subtle anomalies; while at high speeds or sharp acceleration, quotient judgment can more reliably reflect the proportional relationship between sensor data to ensure the stability and accuracy of fault detection.

[0072] In a specific implementation, system degradation measures are used to ensure that when a common cause failure is detected, the vehicle can continue to operate safely, thereby minimizing the safety risks caused by the failure.

[0073] After detecting that the data of the wheel speed sensor and the longitudinal acceleration sensor are out of the allowable range and confirming that there is a common cause failure, the system immediately starts the degraded mode. The specific measures are as follows: First, the system turns off the vehicle's anti-lock braking system (ABS), traction control system (TCS) and vehicle dynamic control system (VDC). These control systems provide vehicle stability support while relying on sensor data, but when the data is unreliable due to sensor failure, turning off these systems can prevent erroneous control signals and prevent the vehicle from continuing to operate in an uncontrolled state, thereby reducing potential safety risks.

[0074] Secondly, to further ensure the controllability of the vehicle in the event of a fault, the system limits the vehicle's maximum speed and acceleration to ensure that they are within a safe range. By reducing the vehicle's speed and acceleration, the system can reduce the vehicle's kinetic energy and inertia in the fault state, making it easier for the driver to control it in an emergency and reducing the possibility of an accident.

[0075] At the same time, the system will send out a fault alarm to the driver, reminding the driver through the warning light on the dashboard or sound that the current vehicle may have a sensor failure or system failure. This alarm can help the driver realize the change of vehicle status in time and take more cautious driving measures.

[0076] Finally, the system sends the fault information to the vehicle's central control bus (CAN) to record the fault details and provide data support for subsequent maintenance and diagnosis. By storing the fault information on the CAN bus, maintenance personnel can quickly query the fault record during subsequent maintenance, so as to more effectively analyze the source of the problem and take necessary repair measures.

[0077] Through these degradation measures, the system can maximize the safety of the vehicle and the driver when a sensor fails, ensuring the controllability and safety of the vehicle in the fault state.

[0078] In a specific embodiment, when a common cause failure of a wheel speed sensor is detected, the system not only degrades but also further initiates an alternative control strategy to maintain vehicle stability under limited conditions.

[0079] When it is confirmed that the wheel speed signal is unreliable due to a common cause failure, the system uses other vehicle dynamic data (such as steering wheel angle and yaw rate) to replace the wheel speed data, thereby adjusting the vehicle dynamic control system in response. The specific steps are as follows: First, the system detects and collects data on the steering wheel angle and yaw rate. The steering wheel angle provides information about the vehicle's steering state, while the yaw rate reflects the vehicle's rotation rate around the vertical axis. These data can be used as an alternative reference for vehicle stability control when the wheel speed signal is missing or unreliable.

[0080] Next, the system uses the data of steering wheel angle and yaw rate to adjust the vehicle dynamic control system. For example, during the steering process, according to the change of steering wheel angle, the system can appropriately adjust the braking force distribution of the wheels to ensure the stability of the vehicle when turning. At the same time, the system uses the yaw rate data to determine whether the vehicle has a tendency to oversteer or understeer, and performs corresponding braking intervention to prevent the vehicle from becoming unstable at high speed or in sharp turns.

[0081] Through this control strategy based on other dynamic data, the system can still provide minimum stability support to ensure the basic safety of the vehicle when wheel speed data is unavailable. This alternative control strategy provides effective protection for vehicle stability after common cause failure of wheel speed sensor.

[0082] Based on the same inventive concept as the aforementioned embodiments, an embodiment of the present invention provides a vehicle fault diagnosis device, which realizes real-time monitoring and fault diagnosis of the dynamic state of the vehicle through the coordinated work of dynamic sensor components, thereby effectively ensuring the driving safety of the vehicle.

[0083] The vehicle fault diagnosis device includes a dynamic sensor component for collecting various dynamic signals of the vehicle. The dynamic sensor component is composed as follows: Wheel speed sensor: The wheel speed sensor is installed on each wheel. It generates a pulse signal by detecting the rotation of the ring gear to obtain the wheel speed. The system processes the speed sensor signal to generate the overall vehicle speed data, providing a basis for subsequent fault diagnosis.

[0084] Acceleration sensor: used to measure the longitudinal and lateral acceleration of the vehicle. It can directly provide dynamic change information of the vehicle during driving, with accurate data and quick response.

[0085] Inertial Measurement Unit (IMU): monitors the multi-dimensional dynamic state of the vehicle, including acceleration and angular velocity information. It consists of a three-axis acceleration sensor and a three-axis gyroscope, providing the dynamic posture and motion characteristics of the vehicle in three-dimensional space.

[0086] Steering wheel angle sensor: detects the steering wheel angle and speed. Outputs vehicle turning status information in real time to assist in determining the vehicle's driving mode (such as straight driving, turning).

[0087] Gyroscope: Independently monitors the angular velocity of the vehicle, especially suitable for detecting the rotational motion characteristics of the vehicle. Provides high-precision angular velocity signals to determine whether the vehicle has abnormal steering behavior.

[0088] Data acquisition and processing module: electrically connected to the dynamic touch sensor assembly; acquires sensor signals in real time and transmits the signals to the data processing module for analysis.

[0089] Fault diagnosis and response module: completes fault diagnosis based on the data collected by the data acquisition and processing module, and triggers the response mechanism when a fault is detected.

[0090] Based on the same inventive concept as the above embodiments, an embodiment of the present invention provides a vehicle equipped with a vehicle fault diagnosis device as claimed in claim 9. By integrating the device, the vehicle can monitor the dynamic state in real time and efficiently detect faults, thereby improving driving safety and reliability.

[0091] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0092] In this document, the terms "comprises," "comprising," or any other variations thereof, are intended to cover a non-exclusive inclusion of elements other than those listed and may also include additional elements not expressly listed.

[0093] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A vehicle fault diagnosis method, characterized in that: include: S1: acquiring a sensing signal of a vehicle dynamic sensor assembly, generating dynamic data of the vehicle according to the sensing signal, and performing consistency processing on the dynamic data to calculate an acceleration deviation value of the vehicle; S2: Determine whether the acceleration deviation value exceeds a preset fault threshold, and if so, record the number of times the acceleration deviation value exceeds the fault threshold; S3: within a preset diagnosis period, if the acceleration deviation value exceeds the fault threshold for a number of times greater than a preset number threshold, the vehicle's power system is triggered to degrade and an alarm is issued.

2. The vehicle fault diagnosis method according to claim 1, characterized in that: The step S1 further includes: acquiring the driving state of the vehicle, and selecting a target sensor of the vehicle dynamic sensor assembly according to the driving state of the vehicle, wherein the target sensor includes at least two sensors in the vehicle dynamic sensor assembly.

3. The vehicle fault diagnosis method according to claim 2, characterized in that: Dynamically adjust the fault threshold and preset diagnostic period according to the vehicle's driving status and the type of target sensor; The driving status includes straight driving, acceleration, deceleration and turning status.

4. The vehicle fault diagnosis method according to claim 2, characterized in that: Perform validity checks on the acquired dynamic data, including: Check whether the acquired sensor data of the same type is within its normal working range; The normal data are weighted averaged, and based on the weighted average result, the data are converted into the same unit for consistency processing.

5. The vehicle fault diagnosis method according to any one of claims 2 to 4, characterized in that: The target sensors are wheel speed sensors and longitudinal acceleration sensors; The step S1 comprises: acquiring a sensing signal of a wheel speed sensor, and generating wheel speed data of the vehicle according to the sensing signal; Acquiring a sensing signal from a longitudinal acceleration sensor, and generating longitudinal acceleration data of the vehicle according to the sensing signal; The consistency processing is: converting the wheel speed data into acceleration data.

6. The vehicle fault diagnosis method according to claim 5, characterized in that: The calculation of the acceleration deviation value includes: calculating the difference between the converted acceleration data and the longitudinal acceleration data to detect the degree of the fault or whether the fault threshold is met.

7. The vehicle fault diagnosis method according to claim 6, characterized in that: The system degradation includes the following measures: Turn off the vehicle's anti-lock braking system (ABS), traction control system (TCS) and vehicle dynamic control system (VDC); Limit the vehicle's maximum speed and acceleration to ensure safety in the event of a malfunction; Provide fault alerts to the driver to indicate possible sensor failure or system failure; The fault information is sent to the vehicle's central control bus (CAN) for recording and facilitating subsequent maintenance.

8. The vehicle fault detection method according to claim 7, characterized in that: The fault diagnosis method includes starting an alternative control strategy based on other dynamic data of the vehicle when a common cause failure of a wheel speed sensor is detected: The vehicle dynamics control system is adjusted based on the vehicle's steering wheel angle, yaw rate and other data to ensure minimal vehicle stability control.

9. A vehicle fault diagnosis device using the vehicle fault diagnosis method according to any one of claims 1 to 8, characterized in that: including a dynamic sensor assembly; Data acquisition and processing module: electrically connected to the dynamic touch sensor assembly; Fault diagnosis and response module: completes fault diagnosis based on the data collected by the data acquisition and processing module, and triggers the response mechanism when a fault is detected; The dynamic sensor assembly includes a wheel speed sensor, an acceleration sensor, an inertial measurement unit, a steering wheel angle sensor and a gyroscope.

10. A vehicle, characterized in that: It comprises the vehicle fault diagnosis device as claimed in claim 9.

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