Brake system fault diagnosis method and brake actuating mechanism fault diagnosis method

Abstract

The invention discloses a brake system fault diagnosis method and a brake actuating mechanism fault diagnosis method, and the method comprises the steps: fusing a multi-signal flow diagram test node for fault diagnosis of a brake system with abnormal detection of detection data of a brake system sensor, and building a multi-signal flow diagram model of the brake system; and judging whether the brake system fault is caused by the fault of the mechanical component or not according to the multi-signal flow graph model. According to the method, the model and the data are fused, and after the multi-signal flow graph model is established, whether the brake actuating mechanism breaks down or not can be quickly determined by combining anomaly detection of the measuring point data and reasoning of the multi-signal flow graph model, so that the problems of limited fault diagnosis means and low diagnosis efficiency of mechanical components such as the brake actuating mechanism and the like are solved; the efficiency of fault diagnosis is improved, and automatic diagnosis and testing of faults of pure mechanical components such as a brake actuating mechanism are realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of fault diagnosis, and in particular relates to a method for diagnosing faults of a brake system and a method for diagnosing faults of a brake actuator. Background Art

[0002] Aircraft braking systems have a significant impact on flight safety. Currently, fault diagnosis of purely mechanical components in the braking system, such as the brake actuator, relies primarily on manual visual inspection to determine the source of the fault. This method suffers from shortcomings such as low inspection efficiency, strong subjectivity, and inadequate inspection, which hinders fault diagnosis of aircraft braking systems.

[0003] To address brake actuator failures, patent document CN112033693A uses a high-definition camera and a sound recognition device to observe the brake pads during vehicle braking to determine whether the brake failure is caused by the brake pads. Although this can replace manual troubleshooting, it requires the configuration of corresponding hardware, increasing the complexity and cost of the system. Patent document CN111391811A uses a temperature sensor and an angle sensor to detect the brake drum temperature and the twisting angle of the brake drive lever. However, due to the limitations of the sensor's detection capabilities, it is unable to identify mechanical deformation failures of the brake drum, which has certain limitations in application and suffers from the same problems mentioned above. Summary of the Invention

[0004] The purpose of the present invention is to provide a brake system fault diagnosis method and a brake actuator fault diagnosis method to solve the problem of effective detection means and low efficiency in fault diagnosis of purely mechanical components in the brake system such as the brake actuator.

[0005] The present invention is achieved through the following technical solutions: A brake system fault diagnosis method integrates a multi-signal flow graph test node used for brake system fault diagnosis with anomaly detection of brake system sensor detection data, establishes a multi-signal flow graph model of the brake system, and determines whether the brake system fault is caused by a fault in a mechanical component based on the multi-signal flow graph model; the sensor is used to detect the status of other components in the brake system except the mechanical component to be detected.

[0006] In some embodiments, the step of obtaining a multi-signal flow graph test node for brake system fault diagnosis includes: Determine the component modules of the brake system in the multi-signal flow graph and the signal transmission relationship between the modules, determine the test nodes required to establish the multi-signal flow graph and the position of the test nodes in the multi-signal flow graph, and bind the test nodes to the modules corresponding to the sensors in the multi-signal flow graph to associate the test nodes with the modules to be tested in the multi-signal flow graph.

[0007] In some embodiments, an FMECA analysis is performed on the brake system to obtain an FMECA analysis table of the brake system, and the correlation relationship between the components of the brake system is obtained based on the FMECA analysis table. The component modules of the brake system and the signal transmission relationship between the modules in the multi-signal flow graph are determined based on the correlation relationship.

[0008] In some embodiments, performing FMECA analysis on the brake system includes analyzing the brake system's failure modes, fault detection methods, fault severity, and fault prevention methods.

[0009] In some embodiments, abnormality detection of brake system sensor detection data includes using a detection algorithm to analyze whether hydraulic brake pressure data collected by a hydraulic pressure sensor, tire speed data collected by a speed sensor, and tire pressure data collected by a tire pressure sensor are abnormal.

[0010] In some embodiments, the test node is bound to the abnormal detection result of the corresponding sensor detection data, and a multi-signal flow graph of the brake system is established according to the component modules of the brake system, the signal transmission relationship between the modules, and the test nodes to obtain a multi-signal flow graph model.

[0011] In some embodiments, the step of determining whether a brake system failure is caused by a mechanical component failure according to the multi-signal flow graph model includes: When a sensor detects a fault in the brake system, it determines the test node corresponding to the fault and, based on the test node, identifies all modules that may cause the fault. According to the abnormal detection results of other sensor detection data, the abnormal state of the corresponding test node is judged; Based on the abnormal status of the test node, it is determined whether the related module has failed, so as to determine whether the brake system failure is caused by the failure of the mechanical component.

[0012] On the other hand, the present invention also provides a brake actuator fault diagnosis method, comprising the following steps: Perform FMECA analysis on the brake system to obtain the FMECA analysis table of the brake system; Obtain the relationship between the components of the brake system according to the FMECA analysis table; Determine the components of the brake system in the multi-signal flow graph and the signal transmission relationship between the modules based on the association relationship, determine the test nodes that need to be set in the multi-signal flow graph and the location of the test nodes in the multi-signal flow graph, and bind the test nodes to the modules corresponding to the sensors in the multi-signal flow graph; The test nodes are integrated with the anomaly detection of sensor detection data, and a multi-signal flow graph model of the braking system is established based on the component modules in the multi-signal flow graph, the signal transmission relationship between the modules, and the test nodes; The brake actuator fault in the brake system is diagnosed based on the multi-signal flow graph model.

[0013] In some embodiments, test nodes are respectively set at the module positions corresponding to the brake control mechanism and the tire, and the test nodes are respectively bound to the hydraulic pressure sensor, the speed sensor, and the tire pressure sensor.

[0014] In some embodiments, when an abnormal wheel speed of a tire is detected, the fault source causing the fault is determined to be a brake control mechanism, a brake actuator, or a tire according to a multi-signal flow graph model; Based on the abnormal detection results of the detection data of the hydraulic pressure sensor and the tire pressure sensor, the fault status of the corresponding test node is judged. When it is judged that the brake control mechanism and the tire are not faulty, the cause of the fault is judged to be a fault of the brake actuator.

[0015] Compared with the prior art, the present invention has the following advantages and beneficial effects: The present invention fuses the model with the data. After establishing a multi-signal flow graph model, it combines the anomaly detection of the measurement point data with the reasoning of the multi-signal flow graph model to quickly determine whether a brake actuator has failed. This solves the problems of limited fault diagnosis methods and low diagnostic efficiency for mechanical components such as brake actuators, improves the efficiency of fault diagnosis, and realizes the automated diagnosis and testing of faults of pure mechanical components such as brake actuators, providing a new idea for fault diagnosis of mechanical components in brake systems.

[0016] The present invention adopts a diagnostic method that integrates a multi-signal flow graph model with data anomaly detection, takes the multi-signal flow graph model as the fault reasoning subject, and uses data anomaly detection as the measurement point implementation source, which can achieve rapid and objective fault diagnosis.

[0017] The sensors of the brake system are used as measuring points, and an anomaly detection algorithm is used to detect anomalies in the sensor collected data, and then the measuring point status is determined. No additional complex electrical tests are required, avoiding the traditional test method of applying input stimulus and monitoring output at the traditional multi-signal flow graph measuring points, thus saving testing and diagnostic resources.

[0018] By utilizing the existing sensors of the brake system to collect data and performing detection and analysis based on the collected data, fault diagnosis of the brake actuator can be achieved without adding other hardware. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings in the embodiments will be briefly introduced below. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0020] Figure 1 Schematic diagram of a multi-signal flow graph model of a brake system established in an embodiment of the present invention. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solutions and advantages of the present application clearer, the specific embodiments of the present application are further described in detail below in conjunction with the accompanying drawings. It is understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. It should also be noted that, for ease of description, only parts related to the present application, not all of the contents, are shown in the accompanying drawings. Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flow charts. Although the flow charts describe each operation (or step) as a sequential process, many of the operations therein can be implemented in parallel, concurrently or simultaneously. In addition, the order of the operations can be rearranged. The process can be terminated when its operation is completed, but can also have additional steps not included in the accompanying drawings. The process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0022] The present invention adopts a method that integrates a multi-signal flow graph model with data detection to diagnose faults of the brake actuator. An FMECA analysis table is obtained by performing FMECA analysis on the brake system, and a multi-signal flow graph is modeled based on the FMECA analysis table. The multi-signal flow graph model is used as the fault reasoning subject, and data anomaly detection is used as the measurement point implementation source to realize fault diagnosis of the brake actuator system.

[0023] FMECA targets all possible failures of a product and, based on the analysis of failure modes, determines the impact of each failure mode on product operation, identifies single-point failures, and determines the harmfulness of the failure mode according to its severity and probability of occurrence.

[0024] In some embodiments of the present invention, a brake system fault diagnosis method integrates a multi-signal flow graph test node used for brake system fault diagnosis with anomaly detection of brake system sensor detection data to establish a multi-signal flow graph model of the brake system, and determines whether the brake system fault is caused by a fault of a mechanical component based on the multi-signal flow graph model; the sensor is used to detect the status of other components in the brake system except the mechanical component to be detected.

[0025] In some embodiments, the step of obtaining a multi-signal flow graph test node for brake system fault diagnosis includes: Determine the component modules of the brake system in the multi-signal flow graph and the signal transmission relationship between the modules, determine the test nodes required to establish the multi-signal flow graph and the position of the test nodes in the multi-signal flow graph, and bind the test nodes to the modules corresponding to the sensors in the multi-signal flow graph to associate the test nodes with the modules to be tested in the multi-signal flow graph.

[0026] In some embodiments, an FMECA analysis is performed on the brake system to obtain an FMECA analysis table of the brake system, and the correlation relationship between the components of the brake system is obtained based on the FMECA analysis table. The component modules of the brake system and the signal transmission relationship between the modules in the multi-signal flow graph are determined based on the correlation relationship.

[0027] In some embodiments, performing FMECA analysis on the brake system includes analyzing the brake system's failure modes, fault detection methods, fault severity, and fault prevention methods.

[0028] In some embodiments, abnormality detection of brake system sensor detection data includes using a detection algorithm to analyze whether hydraulic brake pressure data collected by a hydraulic pressure sensor, tire speed data collected by a speed sensor, and tire pressure data collected by a tire pressure sensor are abnormal.

[0029] In some embodiments, the test node is bound to the abnormal detection result of the corresponding sensor detection data, and a multi-signal flow graph of the brake system is established according to the component modules of the brake system, the signal transmission relationship between the modules, and the test nodes to obtain a multi-signal flow graph model.

[0030] In some embodiments, the step of determining whether a brake system failure is caused by a mechanical component failure according to the multi-signal flow graph model includes: When a sensor detects a fault in the brake system, it determines the test node corresponding to the fault and, based on the test node, identifies all modules that may cause the fault. According to the abnormal detection results of other sensor detection data, the abnormal state of the corresponding test node is judged; Based on the abnormal status of the test node, it is determined whether the related module has failed, so as to determine whether the brake system failure is caused by the failure of the mechanical component.

[0031] The application of the brake actuator fault diagnosis method of the present invention in brake system fault diagnosis is described in detail below with reference to specific embodiments.

[0032] Step 1: Brake system FMECA analysis Perform an FMECA analysis on the brake system. The FMECA analysis is performed from the bottom up, starting with the most basic components. Through FMECA analysis, the impact of various failure modes on the brake system and their proportion can be clearly determined. The FMECA analysis of the brake system can be performed in the following steps: Step 1.1: Get brake system status information Components related to the brake system include brake control mechanism, brake actuator, tires, hydraulic pressure sensor, speed sensor, and tire pressure sensor.

[0033] After receiving the command input, the brake control mechanism outputs hydraulic brake pressure, which acts on the brake actuator and achieves braking through mechanical friction.

[0034] The tires maintain contact with the ground, ensuring reliable support and stability.

[0035] The hydraulic pressure sensor collects the hydraulic brake pressure output by the brake control mechanism in real time; the speed sensor collects the tire wheel speed in real time and outputs the movement speed; the tire pressure sensor collects the tire pressure in real time and outputs the tire pressure value.

[0036] The performance of the brake system is closely related to the status of the brake control mechanism, brake actuator, and tires.

[0037] Step 1.2: Failure mode analysis Analyze the failure modes of different components within the brake system, including: 1) Brake control mechanism: There are two failure modes: no hydraulic brake pressure output and incorrect hydraulic brake pressure output (exceeding / falling below the specified value); 2) Brake actuator: There are two failure modes: sticking and cracking / deformation; 3) Tire: There is one failure mode, rupture; 4) Hydraulic pressure sensor: There are two failure modes: no signal output and incorrect signal output; 5) Speed sensor: There are two failure modes: no signal output and incorrect signal output; 6) Tire pressure sensor: There are two failure modes: no signal output and incorrect signal output.

[0038] Step 1.3: Analyze the detection method of the failure mode When analyzing the detection methods of failure modes in FMECA, it is only necessary to clearly define the general detection methods used without describing the specific implementation details of the detection. The failure mode detection methods used for different components include: 1) Brake control mechanism: electrical testing; 2) Brake actuator: manual visual inspection; 3) Tires: manual visual inspection; 4) Hydraulic pressure sensor: electrical detection; 5) Speed sensor: electrical detection; 6) Tire pressure sensor: electrical detection.

[0039] Step 1.4: Analyze the impact of the fault and the severity of the damage According to the impact and hazards of the failure mode, the severity of the impact and hazards is divided into three categories: severe (complete loss of system functions), moderate (loss of some non-core functions), and mild (functional performance is reduced but not lost).

[0040] Analyze the severity of the brake system failure based on the severity classification, including: 1) The brake control mechanism has no output of hydraulic brake pressure, resulting in the brake actuator being unable to obtain hydraulic brake pressure, the brake function failing, and the brake cannot be braked; Severity level: severe.

[0041] 2) The brake pressure output of the brake control mechanism exceeds or falls below the specified value, causing the brake actuator to be unable to obtain the correct brake pressure, resulting in reduced braking performance and inability to perform correct and effective braking; Severity level: Mild.

[0042] 3) The brake actuator is stuck and cannot generate effective braking force, the braking performance is reduced, and correct and effective braking cannot be performed; Severity level: Mild.

[0043] 4) The brake actuator is cracked or deformed, unable to generate effective braking force, the braking performance is reduced, and the brake cannot be performed correctly and effectively; Severity level: Mild.

[0044] 5) The tire is cracked and cannot maintain good contact with the ground, the braking performance is reduced, and correct and effective braking cannot be performed; Severity level: Mild.

[0045] 6) The hydraulic pressure sensor has no signal output or the output signal is incorrect, and the brake pressure data cannot be collected during the braking process; it does not affect the brake function performance.

[0046] 7) The speed sensor has no signal output or the signal output is incorrect, and the movement speed data during the braking process cannot be collected; it has no impact on the performance of the braking function.

[0047] 8) The tire pressure sensor has no signal output or the signal output is incorrect, and tire pressure data cannot be collected during braking; it does not affect the performance of the braking function.

[0048] Step 1.5: Analyze fault prevention methods Generally speaking, common fault prevention methods include: strengthening inspections, shortening regular inspection cycles, or using components or equipment with higher reliability / quality.

[0049] Step 1.6: Create FMECA analysis table Based on the information and analysis obtained in steps 1.1 to 1.5, an FMECA analysis table is established, as shown in Table 1.

[0050] Table 1 FMECA analysis of brake system failure Step 2: Establish a multi-signal flow graph model of the braking system According to the FMECA analysis table formed by FMECA analysis, the cross-linking relationship between the components in the brake system and the different impact information of various failure modes on the analysis object are obtained, and the multi-signal flow graph is modeled based on the FMECA analysis table.

[0051] The multi-signal flow graph mainly includes basic nodes such as module nodes, test nodes, and nodes, switch nodes, and connection relationships. The multi-signal flow graph model is represented by a specific directed graph: 1) Modules are represented by boxes, and each module node represents a functional module in the system; 2) Test nodes are represented by circles. Each test node represents a test item that is bound to a functional module in the system. 3) AND nodes are represented by "AND logic gate" symbols, which are used to effectively express the redundant structure in the system; 4) Switch nodes are represented by "switch logic" symbols to effectively express the different fault transmission relationships under different operating modes of the system; 5) The connection relationship is a directed edge, which is represented by a line with a one-way arrow. A line pointing from A to B indicates that signals and faults are transmitted from A to B.

[0052] Specifically, the multi-signal flow graph modeling of the brake system includes the following steps: Step 2.1: Determine the structure of the brake system The input basis for the multi-signal flow graph modeling of the brake system is the component structure of the brake system. According to the FMECA analysis table, the component structure of the brake system can be represented as: brake control mechanism C1, brake actuator C2, tire C3, hydraulic pressure sensor C4, speed sensor C5, and tire pressure sensor C6.

[0053] Step 2.2: Determine the signal transmission relationship Determine the signal transmission relationship based on the functional principle of the brake system's structure, including: 1) The brake signal passes through the brake control mechanism, brake actuator, and tire to complete the braking process; the transmission relationship of the brake signal is C1→C2→C3; 2) The hydraulic pressure sensor provides real-time feedback of the hydraulic brake pressure; the pressure signal transmission relationship is C1→C4; 3) The speed sensor provides real-time feedback on the tire speed; the transmission relationship of the speed signal is C3→C5; 4) The tire pressure sensor provides real-time feedback of tire pressure; the transmission relationship of the tire pressure signal is C3→C6.

[0054] Step 2.3, test node analysis Test nodes are located at the top level of the multi-signal flow graph and represent the various failure modes detected by them. A pass for a test node indicates that the failure did not occur, while a fail indicates that the failure did occur.

[0055] The signal transmission relationship in step 2.2 shows that a functional performance failure in the brake system may be caused by a failure in any one or more modules among C1, C2, and C3. Therefore, to locate the cause of the failure, it is necessary to add test nodes that can detect C1, C2, and C3 failures.

[0056] The selection of test nodes complies with limited completeness, that is, to complete the most tests with the least number of test points as much as possible and avoid redundant test points.

[0057] At the same time, not every test node is testable. For example, for C2, the mechanical brake mechanism mostly uses visual inspection to detect whether it is faulty, and there is no electrical testing method. Therefore, this test node is considered untestable.

[0058] Therefore, combined with the settings of the sensors in the brake system, it is determined that different test nodes should be set at C1 and C3, which are: 1) Test node T1: Detects the hydraulic brake pressure and can detect the failure mode of C1; that is, if test node T1 fails, it indicates that C1 is faulty.

[0059] 2) Test node T2: Detection speed, can detect the failure modes of C1, C2, and C3; that is, if test node T2 fails, it indicates that C1, C2, and C3 may all fail.

[0060] 3) Test node T3: Detects tire pressure and can detect the failure mode of C3; that is, if test node T3 fails, it indicates that C3 is faulty.

[0061] Step 2.4, test node binding At the determined test node location, add tests and associate them with detectable component modules.

[0062] Through the analysis in steps 2.2 and 2.3, we can know that: 1) C4 and C1 have a signal association relationship, so binding test node T1 to C4 can associate test node T1 with C1; 2) C5 and C3 have a signal association relationship, so binding test node T2 to C5 can associate test node T2 with C3; 3) C6 and C3 have a signal association relationship, so by binding the test node T3 to C6, the test node T3 can be associated with C3.

[0063] Step 2.5: Model data fusion Different from the traditional multi-signal flow graph test node adding actual test, that is, it requires input stimulus and output monitoring, and determines whether the current test node passes based on the relationship between the output result and the input stimulus; the present invention uses the sensor output data of the brake system, combined with the anomaly detection algorithm, to bind the algorithm detection results with the test analysis of the test node to achieve the fusion of model and data.

[0064] 1) For test node T1, a clustering algorithm can be used to detect anomalies in the "hydraulic brake pressure" data collected by C4 to determine whether typical anomalies such as inaccurate data output and delayed feedback response occur. If an anomaly is detected, test node T1 fails; if no anomaly is detected, test node T1 passes.

[0065] 2) For test node T2, a multivariate autoregressive algorithm is used to detect anomalies in the "speed" data collected by C5 to determine whether the speed drop rate is positively correlated with the given hydraulic brake pressure. If an anomaly is detected, test node T2 fails; if no anomaly is detected, test node T2 passes.

[0066] 3) For test node T3, a clustering algorithm is used to detect anomalies in the "tire pressure" data collected by C6 to determine whether the tire pressure exceeds the normal specified range. If an anomaly is detected, test node T3 fails; if no anomaly is detected, test node T3 passes.

[0067] Through the above method, the data of "hydraulic brake pressure", "speed" and "tire pressure" are integrated with the multi-signal flow graph test nodes.

[0068] Step 2.6: Create a multi-signal flow graph The brake system does not involve AND nodes and switch nodes, so the established multi-signal flow graph does not contain these two node elements.

[0069] After completing the division of component modules and test nodes, finally add connection relationships and connect component modules with component modules and component modules with test nodes according to the structural association relationship.

[0070] like Figure 1 As shown in the figure, the established multi-signal flow graph model of the braking system is shown.

[0071] Step 3: Fault diagnosis reasoning When the brake system is functioning properly, the rate of speed reduction should be positively correlated with the hydraulic brake pressure: the greater the brake pressure, the faster the speed should decrease, and vice versa. Detecting an abnormal speed can be considered a sign of a brake system malfunction. Analyzing the specific cause of the brake system failure requires locating the fault by combining test results from different test nodes with reasoning using multiple signal flow graphs.

[0072] Based on the method of the present invention, the fault diagnosis reasoning process for the brake actuator is as follows: 1) An abnormal wheel speed is detected, meaning the T2 test node fails. Based on the multi-signal flow graph model, the fault sources that caused the test failure are likely the brake control mechanism C1, the brake actuator C2, or the tire C3. 2) Test T1: If the test node T1 passes, it means that the brake control mechanism C1 is not faulty; 3) Test T3: If test node T3 passes, it means that tire C3 is fault-free; 4) It can be inferred that the brake actuator C2 is faulty.

[0073] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Any simple modification or equivalent change made to the above embodiment based on the technical essence of the present invention shall fall within the scope of protection of the present invention.

Claims

1. A brake system fault diagnosis method, characterized in that: The multi-signal flow graph test nodes used for brake system fault diagnosis are integrated with the anomaly detection of brake system sensor detection data to establish a multi-signal flow graph model of the brake system. Based on the multi-signal flow graph model, it is determined whether the brake system fault is caused by a fault in a mechanical component; the sensor is used to detect the status of other components in the brake system except the mechanical component to be detected.

2. The brake system fault diagnosis method according to claim 1, characterized in that: The steps for obtaining a multi-signal flow graph test node for brake system fault diagnosis include: Determine the component modules of the brake system in the multi-signal flow graph and the signal transmission relationship between the modules, determine the test nodes required to establish the multi-signal flow graph and the position of the test nodes in the multi-signal flow graph, and bind the test nodes to the modules corresponding to the sensors in the multi-signal flow graph to associate the test nodes with the modules to be tested in the multi-signal flow graph.

3. The brake system fault diagnosis method according to claim 2, characterized in that: Perform FMECA analysis on the brake system to obtain the FMECA analysis table of the brake system. According to the FMECA analysis table, the correlation between the components of the brake system is obtained. According to the correlation, the component modules of the brake system and the signal transmission relationship between the modules in the multi-signal flow graph are determined.

4. The brake system fault diagnosis method according to claim 3, characterized in that: FMECA analysis of the brake system includes analysis of the brake system failure mode, fault detection method, fault severity, and fault prevention method.

5. The brake system fault diagnosis method according to claim 1, characterized in that: The abnormality detection of the brake system sensor detection data includes using a detection algorithm to analyze whether the hydraulic brake pressure data collected by the hydraulic pressure sensor, the tire speed data collected by the speed sensor, and the tire pressure data collected by the tire pressure sensor are abnormal.

6. The brake system fault diagnosis method according to claim 2, characterized in that: The test nodes are bound to the anomaly detection results of the corresponding sensor detection data, and a multi-signal flow graph of the brake system is established according to the component modules of the brake system, the signal transmission relationship between the modules, and the test nodes to obtain a multi-signal flow graph model.

7. The brake system fault diagnosis method according to claim 2, characterized in that: The steps of determining whether a brake system failure is caused by a mechanical component failure according to the multi-signal flow graph model include: When a sensor detects a fault in the brake system, it determines the test node corresponding to the fault and, based on the test node, identifies all modules that may cause the fault. According to the abnormal detection results of other sensor detection data, the abnormal state of the corresponding test node is judged; Based on the abnormal status of the test node, it is determined whether the associated module has failed, so as to determine whether the brake system failure is caused by the failure of the mechanical component.

8. A method for diagnosing a fault of a brake actuator, characterized in that: The following steps are involved: Perform FMECA analysis on the brake system to obtain the FMECA analysis table of the brake system; Obtain the relationship between the components of the brake system according to the FMECA analysis table; Determine the components of the brake system in the multi-signal flow graph and the signal transmission relationship between the modules based on the association relationship, determine the test nodes that need to be set in the multi-signal flow graph and the location of the test nodes in the multi-signal flow graph, and bind the test nodes to the modules corresponding to the sensors in the multi-signal flow graph; The test nodes are integrated with the anomaly detection of sensor detection data, and a multi-signal flow graph model of the braking system is established based on the component modules in the multi-signal flow graph, the signal transmission relationship between the modules, and the test nodes; The brake actuator fault in the brake system is diagnosed based on the multi-signal flow graph model.

9. The brake actuator fault diagnosis method according to claim 8, characterized in that: Test nodes are set up at the module positions corresponding to the brake control mechanism and tire, and the test nodes are bound to the hydraulic pressure sensor, speed sensor, and tire pressure sensor respectively.

10. The brake actuator fault diagnosis method according to claim 8, characterized in that: When an abnormal wheel speed of a tire is detected, the fault source is determined to be a brake control mechanism, a brake actuator or a tire according to a multi-signal flow graph model; Based on the abnormal detection results of the detection data of the hydraulic pressure sensor and the tire pressure sensor, the fault status of the corresponding test node is judged. When it is judged that the brake control mechanism and the tire are not faulty, the cause of the fault is judged to be a fault of the brake actuator.

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