Fault self-diagnosis system and method for an electronic control system of a motorcycle

Abstract

The present invention relates to the technical field of electronic control fault diagnosis, and specifically to a fault self-diagnosis system and method for a motorcycle electronic control system, including a data acquisition module for detecting data according to the motorcycle electronic control system; a first fault diagnosis module for constructing first diagnostic data based on the detection trigger frequency, duration, and execution data, processing the first diagnostic data to obtain first fault data, and obtaining a fault diagnosis result of the ECU control unit according to a first preset diagnostic standard; a second fault diagnosis module for constructing second diagnostic data based on the operating parameters collected by the first detection and the second detection, processing the second diagnostic data to obtain second fault data, and obtaining a motorcycle fault diagnosis result according to a second preset diagnostic standard.

Description

Technical Field

[0001] The present invention relates to the technical field of electronic control fault diagnosis, and specifically to a fault self-diagnosis system and method for a motorcycle electronic control system. Background Art

[0002] In the existing fault diagnosis technology of motorcycle electronic control systems, there are many technical bottlenecks. Traditional diagnosis methods often rely on manual experience and point-by-point troubleshooting, resulting in low diagnosis efficiency. Especially in complex electronic control systems, it is time-consuming and laborious to gradually locate problems. In addition, due to the lack of network support in traditional diagnosis means, it is difficult to share data between systems, and it is difficult to achieve remote collaborative diagnosis, which further exacerbates the difficulty of problem troubleshooting. On the other hand, current systems often lack intelligent means and do not integrate expert systems or AI assistance functions, resulting in insufficient ability to judge complex or hidden faults.

[0003] Due to the lack of a dynamic monitoring mechanism, the real-time performance of the system is poor. It is often unable to quickly capture faults after they occur, which is likely to cause further equipment damage or safety hazards. Due to the large differences in motorcycle models, the data standards of electronic control systems are not unified, which further increases the development difficulty of the diagnosis system. The above problems jointly restrict the efficiency and accuracy of fault diagnosis of motorcycle electronic control systems. There is an urgent need for a more efficient, intelligent, real-time and easily expandable fault self-diagnosis system and method for motorcycle electronic control systems to solve the above problems.

[0004] Therefore, a fault self-diagnosis system and method for a motorcycle electronic control system are proposed. Summary of the Invention

[0005] The purpose of the present invention is to provide a fault self-diagnosis system and method for a motorcycle electronic control system to achieve efficient fault diagnosis of the motorcycle electronic control system under extremely cold weather conditions. The data acquisition module performs data detection according to the motorcycle electronic control system; the first fault diagnosis module constructs first diagnosis data based on the detection trigger frequency, duration, and execution data, processes the first diagnosis data to obtain first fault data, and obtains the fault diagnosis result of the ECU control unit according to the first preset diagnosis standard; the second fault diagnosis module constructs second diagnosis data based on the operating parameters collected by the first detection and the second detection, processes the second diagnosis data to obtain second fault data, and obtains the motorcycle fault diagnosis result according to the second preset diagnosis standard.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A fault self-diagnosis system for a motorcycle electronic control system, comprising:

[0008] When the motorcycle electronic control system is free of faults, the data acquisition module constructs a first detection based on the working state of the ECU control unit and performs operation parameter detection; when the motorcycle electronic control system has faults, the data acquisition module constructs a second detection based on the abnormal temperature change rate and preferentially detects the operation parameters of sensors and actuators related to temperature.

[0009] Further, the system further includes a data storage module, which is used to store the acquired data. The acquired data includes first diagnostic data and second diagnostic data. The first diagnostic data includes the first detection trigger frequency, the first detection response duration, the first detection duration, and the first detection execution result of the ECU control unit, and also includes the second detection trigger frequency, the second detection response duration, the second detection duration, and the second detection execution result of the ECU control unit.

[0010] The second diagnostic data represents the specific data collected by the first detection and the second detection, including front fork data, suspension data, and braking data.

[0011] Further, the first detection includes:

[0012] The first detection is that the electronic control system actively detects the data of the motorcycle; before the first detection is triggered, first evaluate the current load and working state of the ECU control unit. If the ECU is in a high-load or unstable state, delay the trigger of the active detection until the ECU load drops to an appropriate level.

[0013] Calculate the dynamic maximum allowable trigger frequency according to the current ambient temperature and the working state of the ECU.

[0014] Perform the first detection according to the maximum allowable trigger frequency and the trigger threshold of the first detection.

[0015] Further, the second detection includes:

[0016] Under extremely cold conditions, increase the monitoring of the ambient temperature change rate in the sensor area.

[0017] When the ambient temperature change rate exceeds the threshold, even if other parameters do not reach the trigger condition of the second detection, force the trigger of the passive detection.

[0018] After the passive detection is triggered, sort according to the sensitivity of the sensors to temperature, and preferentially detect the sensors and actuators related to temperature.

[0019] The first fault diagnosis module constructs the first diagnostic data based on the detection trigger frequency, duration, and execution data, processes the first diagnostic data to obtain the first fault data, and obtains the fault diagnosis result of the ECU control unit according to the first preset diagnostic standard.

[0020] Further, the first fault diagnosis module includes:

[0021] When no fault occurs during the detection process, perform Laplace transform on the first diagnostic data of the first detection and the second detection, extract the periodic characteristics in the extremely cold environment, and obtain the first fault data of the ECU control unit. The first fault data includes the trigger time, duration, and interval period.

[0022] The first diagnostic criterion includes: matching and comparing the first fault data in the extremely cold state with the normal diagnostic data in the normal temperature state, and combining the proportion of the first diagnostic data occupied by the first detection and the second detection to determine whether there is a fault in the ECU control unit in the extremely cold state; comparing the similarity of the sensor signal waveforms between the extremely cold and normal temperature conditions, and determining it as a low-temperature specific fault when the DTW distance is greater than the preset threshold.

[0023] When a fault occurs during the detection process, obtain the parameter values of the motorcycle electronic control system and mark them as the first fault values. Obtain the first fault range through the first fault values, and construct the first fault set; construct the historical first diagnostic value set according to the historical data of the motorcycle electronic control system; compare each first diagnostic value in the historical first diagnostic value set with the first fault values in the first fault set one by one, and mark the fault type of the motorcycle electronic control system through the comparison results.

[0024] The second fault diagnosis module constructs second diagnostic data based on the operation parameters collected by the first detection and the second detection, processes the second diagnostic data to obtain second fault data, and obtains the motorcycle fault diagnosis result according to the second preset diagnostic criterion.

[0025] Further, the second fault diagnosis module includes:

[0026] The second fault diagnosis module constructs second diagnostic data based on the operation parameters collected by the first detection and the second detection, performs data cleaning and processing on the second diagnostic data to obtain second fault data, and obtains the motorcycle fault diagnosis result according to the second preset diagnostic criterion.

[0027] The second preset diagnostic criterion includes: obtaining the parameter values at the position of the second fault data and marking them as the second fault values, obtaining the second fault range through the second fault values, and constructing the second fault set; constructing the historical second diagnostic value set according to the historical data of all sensors of the motorcycle; comparing each second diagnostic value in the historical second diagnostic value set with the second fault values in the second fault set one by one, and marking the fault type of the motorcycle sensors through the comparison results.

[0028] The present invention also provides a fault self-diagnosis method for a motorcycle electronic control system, including:

[0029] When there is no fault in the motorcycle electronic control system, a first detection is constructed based on the working state of the ECU control unit, and the operating parameters are detected; when a fault occurs in the motorcycle electronic control system, a second detection is constructed based on the abnormal temperature change rate, and the operating parameters of the sensors and actuators related to temperature are preferentially detected;

[0030] The first diagnostic data is constructed according to the detection trigger frequency, duration, and execution data, and the first diagnostic data is processed to obtain the first fault data. According to the first preset diagnostic standard, the fault diagnosis result of the ECU control unit is obtained;

[0031] The second diagnostic data is constructed based on the operating parameters collected by the first detection and the second detection, the second diagnostic data is processed to obtain the second fault data, and the motorcycle fault diagnosis result is obtained according to the second preset diagnostic standard.

[0032] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0033] 1. After evaluating the ECU load and working state, the maximum allowable trigger frequency is dynamically calculated according to the ambient temperature, and active detection is only performed when the ECU is in a low-load and stable state, thus effectively avoiding errors introduced by high load or instability during startup; in case of faults or extremely cold conditions, passive detection is forced to trigger through the abnormal temperature change rate, and the sensors are prioritized according to the temperature sensitivity, which can quickly capture temperature-related abnormal conditions; through the combination of the first detection and the second detection, intelligent adaptive monitoring in different operating states is realized.

[0034] 2. By performing Laplace transform on the first diagnostic data, extracting the periodic characteristics under extremely cold conditions, and comparing the similarity of the characteristics with the normal data under normal temperature conditions using the DTW algorithm, the abnormal conditions of the ECU control unit in a low-temperature environment can be accurately identified; when a fault occurs, by obtaining real-time parameters and constructing a fault set, and then comparing them with the historical diagnostic data one by one, the fault type can be accurately marked and located, which not only improves the accuracy and real-time performance of fault detection, but also provides solid data support for subsequent preventive maintenance and system optimization.

[0035] 3. Through comprehensive analysis and data cleaning of the operating parameters collected by the first detection and the second detection, accurate second diagnostic data is constructed, and the historical diagnostic data is compared using the second preset diagnostic standard, so as to be able to more accurately locate the fault areas of motorcycle sensors and actuators, which not only improves the accuracy and real-time performance of fault detection, but also provides a reliable basis for preventive maintenance, discovers potential problems in a timely manner, reduces maintenance costs, and ensures the stable operation of the motorcycle electronic control system under various working conditions. Brief Description of the Drawings

[0036] Figure 1Schematic diagram of the structure of a fault self-diagnosis system for a motorcycle electronic control system provided by an embodiment of the present invention;

[0037] Figure 2 Flowchart of the operation of the first fault diagnosis module provided by an embodiment of the present invention;

[0038] Figure 3 Flowchart of a fault self-diagnosis method for a motorcycle electronic control system provided by an embodiment of the present invention. Specific embodiments

[0039] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention. Embodiment 1:

[0040] The electronic control system of a motorcycle is not the same as that of a common automobile. First, the ECU control unit of a motorcycle is generally designed to be miniaturized, and its weight is generally less than 200 g, while the electronic control system of a common automobile is a large-scale integration, and its weight is generally greater than 800 g; the power supply of the motorcycle electronic control system is generally a single power supply architecture, while the power supply system of a common automobile is a dual power supply; the heat dissipation method of the motorcycle electronic control system is generally passive heat dissipation, relying on air convection, while the electronic control system of a common automobile has an independent heat dissipation module; most of the sensors of a motorcycle are exposed to the air and are subjected to extreme cold and heat shocks. Especially under extreme conditions, the electronic control system may be damaged.

[0041] In order to detect whether there is a fault in the motorcycle electronic control system at extremely low temperatures, a certain company introduced a fault self-diagnosis system for a motorcycle electronic control system provided by the present invention to improve the real-time monitoring accuracy and efficiency of the motorcycle electronic control system at low temperatures. The system structure is as Figure 1 shown, and the specific implementation is as follows:

[0042] When there is no fault in the motorcycle electronic control system, the data acquisition module constructs a first detection based on the working state of the ECU control unit and performs operation parameter detection; when there is a fault in the motorcycle electronic control system, the data acquisition module constructs a second detection based on the abnormal temperature change rate and preferentially detects the operation parameters of the sensors and actuators related to temperature;

[0043] Further, the data storage module is used to store the collected data, and the collected data includes first diagnostic data and second diagnostic data. The first diagnostic data includes a first detection trigger frequency, a first detection response duration, a first detection duration, a first detection execution result, and also includes a second detection trigger frequency, a second detection response duration, a second detection duration, a second detection execution result, etc.;

[0044] The second diagnostic data represents the specific data collected by the first detection and the second detection, including front fork data, suspension data, and braking data.

[0045] Further, the detection trigger frequency represents the frequency at which the electronic control system performs data detection on the actuators of the sensors on the motorcycle. The detection response duration represents the time from when the electronic control system issues a detection instruction to when the sensors and actuators start to execute. The detection duration represents the time from when the electronic control system issues a detection instruction to when it receives the detection data. The detection execution result represents whether the electronic control system receives specific data after issuing the detection command. If specific data is received, the detection execution result is true, represented by 1. If no specific data is received, the detection execution result is false, represented by 0.

[0046] Further, the second diagnostic data represents the specific data collected by the first and second detections, including not only front fork data, suspension data, and braking data, etc., but also the data of related components collected by the actuators and sensors, including oxygen sensor data, crankshaft sensor data, and electronic throttle data, etc.

[0047] By recording the operating states of the key components of the motorcycle in real time, not only can the dynamic data such as the response duration, trigger frequency, and detection duration between the sensors and actuators be captured in a timely manner, but also the specific data of related components such as the front fork, suspension, braking, oxygen sensor, crankshaft sensor, and electronic throttle can be recorded; through the integrated analysis of these data, the electronic control system can more accurately determine whether a fault exists and the specific area of the fault, so as to achieve fault warning, precise positioning, and preventive maintenance. This not only improves the overall safety and stability of the motorcycle, but also provides solid data support for subsequent data-driven maintenance and optimization tuning.

[0048] Further, the first detection includes:

[0049] The first detection is that the electronic control system actively detects the data of the motorcycle; before the first detection trigger, first evaluate the current load and working state of the ECU control unit. If the ECU is in a high-load or unstable state (such as just starting or performing other important operations), then delay the trigger of the active detection until the ECU load drops to an appropriate level;

[0050] Calculate the dynamic maximum allowable trigger frequency according to the current ambient temperature and the working state of the ECU;

[0051] Perform the first detection according to the maximum allowable trigger frequency and the trigger threshold of the first detection.

[0052] Furthermore, construct a load score to measure the load status. The formula for the load score is:

[0053] ;

[0054] Wherein, represents the load score, represents the CPU load weight coefficient of, represents the memory occupancy rate weight coefficient of, represents the bus load weight coefficient of, represents the ECU chip junction temperature weight coefficient of, represents the maximum allowable junction temperature.

[0055] Table 1. Operating status of the electronic control system

[0056]

[0057] Furthermore, if the load score is greater than the preset threshold, delay the first detection. As shown in Table 1, the load scores detected at some times can be found, and it can be seen that the electronic control system is in a high-load stage, and the first detection is delayed.

[0058] Furthermore, if the load score is not greater than the preset threshold, perform the first detection. According to the current ambient temperature and the operating status of the ECU, calculate the maximum allowable trigger frequency. The formula is:

[0059] ;

[0060] Wherein, represents the maximum allowable trigger frequency, represents the basic detection frequency, represents the temperature attenuation coefficient, represents the ECU chip junction temperature, represents the calibrated temperature, represents the maximum allowable junction temperature, represents the ambient temperature attenuation coefficient, represents the ambient temperature, represents the critical ambient temperature, represents the minimum function.

[0061] Further, the trigger value for the first detection is the preset threshold for the load score. If the load score is not greater than the preset threshold, the first detection is triggered and the first detection is performed according to the maximum allowable trigger frequency. If the load score is greater than the preset threshold, the first detection is not performed.

[0062] Through the above content, intelligent adaptive detection can be realized. Without interfering with the key tasks of the ECU, it is ensured that the fault detection is triggered only when the system load is low and the temperature is appropriate. This not only avoids errors and data noise caused by detection under high load or high temperature, but also reduces the risk of the ECU being under additional pressure due to frequent detection. By calculating the maximum allowable trigger frequency and using the load score to judge the current state of the system, it is ensured that the detection is carried out at the optimal time, thereby improving the accuracy and response efficiency of fault detection, while prolonging the service life of the ECU and related components, and enhancing the overall safety and stability of the motorcycle.

[0063] Further, the second detection includes:

[0064] Under extremely cold conditions, the monitoring of the environmental temperature change rate in the sensor area is increased;

[0065] When the environmental temperature change rate exceeds the threshold, even if other parameters do not meet the conditions for triggering the second detection, passive detection is also forced to be triggered;

[0066] After the passive detection is triggered, the sensors are sorted according to their sensitivity to temperature, and the sensors and actuators related to temperature are preferentially detected.

[0067] Further, the temperatures of the sensors in multiple regions are collected, including the environmental temperature, the engine compartment, the battery compartment, etc., real-time data is collected, and the local temperature change rate of this region is calculated. In this embodiment, the local temperature change rate represents the sensitivity of the sensor to temperature;

[0068] Further, after calculating the local temperature change rates of all regions, they are sorted according to the absolute value of the local temperature change rate, and the sensors and actuators related to temperature are preferentially detected, and all the data of this region are obtained, including the operation data and fault data of the motorcycle, such as the coolant temperature sensor, the fuel heater, etc., because these components are more likely to fail under extremely cold conditions.

[0069] Through the real-time monitoring of the change rates of the temperatures of the sensors in multiple regions and the method of sorting and detecting the sensor data according to the temperature change rate, the fault diagnosis efficiency and reliability of the motorcycle electronic control system in extremely cold environments are significantly improved, and the adaptability to the environment is enhanced; by sorting and detecting according to the sensitivity to temperature, the diagnosis of vulnerable components such as the coolant sensor, the fuel heater, and the brake pads is preferentially guaranteed, resulting in a significant increase in the fault diagnosis rate of key parts.

[0070] The first fault diagnosis module constructs first diagnostic data based on the detection trigger frequency, duration, and execution data, processes the first diagnostic data to obtain first fault data, and obtains the fault diagnosis result of the ECU control unit according to the first preset diagnostic standard; the working process of the first fault diagnosis module is as Figure 2 shown;

[0071] Further, the first fault diagnosis module includes: when no fault occurs during the detection process, performing Laplace transform on the first diagnostic data of the first detection and the second detection, extracting the periodic characteristics in the extremely cold environment to obtain the first fault data of the ECU control unit, and the first fault data includes the trigger moment, duration, and interval period;

[0072] The first diagnostic standard includes: matching and comparing the first fault data in the extremely cold state with the normal diagnostic data in the normal temperature state, and combining the proportion of the first diagnostic data occupied by the first detection and the second detection to determine whether there is a fault in the ECU control unit in the extremely cold state; comparing the similarity of the sensor signal waveforms between the extremely cold and normal temperatures, and when the DTW distance is greater than the preset threshold, it is determined as a low-temperature specific fault;

[0073] When a fault occurs during the detection process, obtain the parameter values of the motorcycle electronic control system and mark them as the first fault values, obtain the first fault range through the first fault values, and construct the first fault set; construct the historical first diagnostic value set according to the historical data of the motorcycle electronic control system; compare each first diagnostic value in the historical first diagnostic value set with the first fault values in the first fault set one by one, and mark the fault type of the motorcycle electronic control system according to the comparison result.

[0074] Further, the first detection and the second detection of the motorcycle electronic control system have a triggering rule, and the triggering rule of the first detection and the second detection can be extracted through Laplace transform to obtain the first fault data of a similar frequency band;

[0075] Further, first calculate the proportion of the diagnostic data occupied by the first detection and the second detection. If the proportion of the first diagnostic data from the second detection is larger, it means that the motorcycle electronic control system often has problems; then obtain the first fault data and the sensor signal waveform of the electronic control system at normal temperature, and then perform waveform matching according to the dynamic time warping (DTW). If the DTW distance is greater than the preset threshold and the proportion of the first diagnostic data from the second detection exceeds the preset threshold, it means that the electronic control system of the motorcycle has a low-temperature specific fault, and the too low temperature will cause the electronic control system to fail to work properly.

[0076] Further, all parameter values of the motorcycle electronic control system are collected and marked as the first fault values, and then compared with the data in the historical first diagnosis value set. According to the degree of difference, the existing faults can be identified. Table 2 shows the data collected by the electronic control system of the motorcycle and the possible fault locations.

[0077] Table 2. Fault Judgment Results of Motorcycle Electronic Control System

[0078]

[0079] The first fault diagnosis module analyzes the trigger frequency, execution duration, and data characteristics of the first detection and the second detection, constructs the first diagnostic data including the trigger moment, duration, and interval period, and performs Laplace transform on it to extract the periodic characteristics in the extremely cold environment. Combining the proportion of the second detection data, it dynamically evaluates the system health status; when the proportion of the second detection data exceeds the preset threshold, it indicates that the system frequently triggers temperature-related diagnoses. Further, the dynamic time warping (DTW) algorithm is used to compare the waveform similarity of the sensor signals under extremely cold and normal temperatures. If the DTW distance exceeds the preset range, it is determined that there are low-temperature specific faults; at the same time, the module continuously collects all parameters of the electronic control system and marks them as the first fault values. By performing multi-dimensional comparison with the data in the historical first diagnosis value set, specific fault types such as oxygen sensor temperature drift and abnormal crankshaft signal gap are identified according to the degree of difference, so as to effectively distinguish the transient anomalies caused by extremely cold from the permanent hardware damage, and ensure that the diagnosis results cover both the environmental adaptability and the compound faults of the system's inherent defects.

[0080] The second fault diagnosis module constructs the second diagnostic data based on the operating parameters collected by the first detection and the second detection, processes the second diagnostic data to obtain the second fault data, and obtains the motorcycle fault diagnosis result according to the second preset diagnosis criterion.

[0081] Further, the second fault diagnosis module includes:

[0082] The second fault diagnosis module constructs the second diagnostic data based on the operating parameters collected by the first detection and the second detection, performs data cleaning and processing on the second diagnostic data to obtain the second fault data, and obtains the motorcycle fault diagnosis result according to the second preset diagnosis criterion;

[0083] The second preset diagnosis criterion includes: obtaining the parameter value at the location of the second fault data and marking it as the second fault value, obtaining the second fault range through the second fault value, and constructing the second fault set; constructing the historical second diagnosis value set based on the historical data of all sensors of the motorcycle; comparing the second diagnosis values in the historical second diagnosis value set with the second fault values in the second fault set one by one, and marking the fault types of the motorcycle sensors through the comparison results.

[0084] Further, the second diagnostic data includes front fork data, suspension data, braking data, etc., and also includes data of associated components collected by actuators and sensors, including data of oxygen sensors, crankshaft sensors, electronic throttle valves, etc., and includes specific data collected by various sensors on the motorcycle.

[0085] Further, preprocess the second diagnostic data, including operations such as data cleaning, duplicate removal, noise reduction, and smoothing, to obtain second fault data; perform precise fault identification on the second fault data according to the second preset diagnostic criteria, and the specific fault types existing in the sensors of the motorcycle can be obtained. As shown in Table 3, the possible fault locations of the sensors of the motorcycle are shown.

[0086] Table 3. Fault judgment results of the sensors of the motorcycle

[0087]

[0088] The second fault diagnosis module performs data cleaning, noise reduction, and smoothing processing on the front fork data, suspension data, braking data, and operating parameters of key components such as oxygen sensors, crankshaft sensors, and electronic throttle valves collected by the first detection and the second detection, effectively eliminates environmental interference and signal distortion, combines the fault mode matching of the historical second diagnostic value set, can accurately identify the abnormal states of sensors and actuators, reduces the risks of misjudgment and missed detection, and supports preventive maintenance decision-making, and finally realizes the high reliability and long life operation of the motorcycle electronic control system under extreme working conditions.

[0089] The system adopts a hierarchical diagnosis mechanism. When there is no fault, the first detection is actively triggered by the state of the ECU control unit to capture periodic characteristics. When the abnormal temperature change rate exceeds the threshold, the second detection is forcibly triggered and the temperature-sensitive components are preferentially scanned. The abnormal fluctuation law in the low-temperature environment is extracted by combining the Laplace transform, and the waveform differences between the extremely cold and normal temperature signals are compared by the dynamic time warping (DTW) algorithm to accurately identify the faults specific to low temperature; at the same time, a diagnostic value set is constructed based on historical data to judge the fault values of the collected data, accurately locate the position where the fault exists, reduce the fault misjudgment rate, and shorten the diagnostic response time. Embodiment 2:

[0090] The present invention also provides a fault self-diagnosis method for a motorcycle electronic control system. The method flow chart is as Figure 3 shown, and the specific implementation manner is as follows:

[0091] When there is no fault in the motorcycle electronic control system, construct the first detection according to the working state of the ECU control unit and perform operating parameter detection; when there is a fault in the motorcycle electronic control system, construct the second detection according to the abnormal temperature change rate and preferentially detect the operating parameters of the sensors and actuators related to temperature;

[0092] Furthermore, the acquired data of the first detection and the second detection also need to be stored. The acquired data includes the first diagnostic data and the second diagnostic data. The first diagnostic data includes the first detection trigger frequency, the first detection response duration, the first detection duration, and the first detection execution result of the ECU control unit, and also includes the second detection trigger frequency, the second detection response duration, the second detection duration, and the second detection execution result of the ECU control unit, etc.

[0093] The second diagnostic data represents the specific data collected by the first detection and the second detection, including front fork data, suspension data, and braking data.

[0094] Furthermore, the first detection is that the electronic control system actively detects the data of the motorcycle; before the first detection is triggered, the current load and working state of the ECU control unit are first evaluated. If the ECU is in a high-load or unstable state, the trigger of the active detection is delayed until the ECU load drops to an appropriate level; Table 4 shows the working state of the electronic control system during the detection.

[0095] Table 4. Working State of the Electronic Control System

[0096]

[0097] Furthermore, according to the current ambient temperature and the working state of the ECU, the dynamic maximum allowable trigger frequency is calculated.

[0098] Furthermore, the first detection is performed according to the maximum allowable trigger frequency and the trigger threshold of the first detection.

[0099] Furthermore, the second detection includes:

[0100] Under extremely cold conditions, the monitoring of the ambient temperature change rate in the sensor area is increased.

[0101] When the ambient temperature change rate exceeds the threshold, even if other parameters do not meet the conditions for triggering the second detection, the passive detection is also forced to be triggered.

[0102] After the passive detection is triggered, the sensors are sorted according to their sensitivity to temperature, and the sensors and actuators related to temperature are preferentially detected.

[0103] The first diagnostic data is constructed based on the detection trigger frequency, duration, and execution data, and the first diagnostic data is processed to obtain the first fault data. The fault diagnosis result of the ECU control unit is obtained according to the first preset diagnostic standard.

[0104] Further, when no fault occurs during the detection process, perform Laplace transform on the first diagnostic data of the first detection and the second detection, extract the periodic characteristics in the extremely cold environment, and obtain the first fault data of the ECU control unit. The first fault data includes the trigger moment, the duration, and the interval period.

[0105] The first diagnostic criterion includes: matching and comparing the first fault data in the extremely cold state with the normal diagnostic data in the normal temperature state, and combining the proportion of the first diagnostic data occupied by the first detection and the second detection to determine whether there is a fault in the ECU control unit in the extremely cold state; comparing the similarity of the sensor signal waveforms between the extremely cold and normal temperature conditions, and determining it as a low-temperature specific fault when the DTW distance is greater than the preset threshold.

[0106] When a fault occurs during the detection process, obtain the parameter values of the motorcycle electronic control system and mark them as the first fault values. Obtain the first fault range through the first fault values and construct the first fault set; construct the historical first diagnostic value set according to the historical data of the motorcycle electronic control system; compare each first diagnostic value in the historical first diagnostic value set with the first fault values in the first fault set one by one, and mark the fault type of the motorcycle electronic control system through the comparison results.

[0107] Construct the second diagnostic data according to the operating parameters collected by the first detection and the second detection, process the second diagnostic data to obtain the second fault data, and obtain the motorcycle fault diagnosis result according to the second preset diagnostic criterion.

[0108] Further, construct the second diagnostic data according to the operating parameters collected by the first detection and the second detection, perform data cleaning and processing on the second diagnostic data to obtain the second fault data, and obtain the motorcycle fault diagnosis result according to the second preset diagnostic criterion.

[0109] The second preset diagnostic criterion includes: obtaining the parameter values at the position of the second fault data and marking them as the second fault values, obtaining the second fault range through the second fault values and constructing the second fault set; constructing the historical second diagnostic value set according to the historical data of all the motorcycle sensors; comparing each second diagnostic value in the historical second diagnostic value set with the second fault values in the second fault set one by one, and marking the fault type of the motorcycle sensors through the comparison results. Shown in Table 5 are the sensors of motorcycles that may have faults diagnosed under extremely cold conditions.

[0110] Table 5. Diagnostic Results of Some Motorcycle Sensors

[0111]

[0112] Through a fault self-diagnosis method for a motorcycle electronic control system provided by the present invention, accurate detection of the ECU control unit under extremely cold conditions is achieved, and when a fault occurs, the fault location of the electronic control system can be accurately judged, improving the fault detection efficiency.

[0113] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A fault self-diagnosis system for a motorcycle electronic control system, characterized in that, Including: When there is no fault in the motorcycle electronic control system, the data acquisition module constructs a first detection according to the working state of the ECU control unit and performs operation parameter detection; When a fault occurs in the motorcycle electronic control system, the data acquisition module constructs a second detection according to the abnormal temperature change rate and preferentially detects the operation parameters of the sensors and actuators related to temperature; The first fault diagnosis module obtains the fault diagnosis result of the ECU control unit according to the first preset diagnosis criterion; performs Laplace transform on the first diagnosis data of the first detection and the second detection, extracts the periodic characteristics in the extremely cold environment, and obtains the first fault data of the ECU control unit. The first fault data includes the trigger moment, the duration, and the interval period; The first diagnosis criterion includes: matching and comparing the first fault data in the extremely cold state with the normal diagnosis data in the normal temperature state, and judging whether there is a fault in the ECU control unit in the extremely cold state by combining the proportion of the first diagnosis data occupied by the first detection and the second detection; comparing the similarity of the sensor signal waveforms between the extremely cold and normal temperatures, and determining it as a low-temperature specific fault when the DTW distance is greater than the preset threshold; When a fault occurs during the detection process, obtain the parameter values of the motorcycle electronic control system and mark them as the first fault values, obtain the first fault range through the first fault values, and construct a first fault set; construct a historical first diagnosis value set according to the historical data of the motorcycle electronic control system; compare each first diagnosis value in the historical first diagnosis value set with the first fault values in the first fault set one by one, and mark the fault type of the motorcycle electronic control system according to the comparison result; The second fault diagnosis module constructs second diagnosis data according to the operation parameters collected by the first detection and the second detection, processes the second diagnosis data to obtain second fault data, and obtains the motorcycle fault diagnosis result according to the second preset diagnosis criterion.

2. The fault self-diagnosis system of a motorcycle electronic control system according to claim 1, characterized in that, Also including: The data storage module is used to store the collected data. The collected data includes the first diagnosis data and the second diagnosis data. The first diagnosis data includes the first detection trigger frequency, the first detection response duration, the first detection duration, and the first detection execution result of the ECU control unit, and also includes the second detection trigger frequency, the second detection response duration, the second detection duration, and the second detection execution result of the ECU control unit; The second diagnosis data represents the specific data collected by the first detection and the second detection, including front fork data, suspension data, and braking data.

3. The fault self-diagnosis system of a motorcycle electronic control system according to claim 1, characterized in that, The first detection includes: The first detection is that the electronic control system actively detects the data of the motorcycle; before the first detection is triggered, first evaluate the current load and working state of the ECU control unit. If the ECU is in a high-load or unstable state, delay the trigger of the active detection until the ECU load drops to an appropriate level; Calculate the dynamic maximum allowable trigger frequency according to the current ambient temperature and the working state of the ECU; Perform the first detection according to the maximum allowable trigger frequency and the trigger threshold of the first detection.

4. The fault self-diagnosis system of a motorcycle electronic control system according to claim 1, characterized in that, The second detection includes: Under extremely cold conditions, increase the monitoring of the ambient temperature change rate in the sensor area; When the environmental temperature change rate exceeds the threshold, passive detection is forcibly triggered even if other parameters do not meet the second detection condition. After passive detection is triggered, sensors are sorted according to their sensitivity to temperature, and sensors and actuators related to temperature are preferentially detected.

5. The fault self-diagnosis system of a motorcycle electronic control system according to claim 1, characterized in that, The second fault diagnosis module includes: The second fault diagnosis module constructs second diagnostic data based on the operating parameters collected from the first detection and the second detection, cleans and processes the second diagnostic data to obtain second fault data, and obtains the fault diagnosis result of the motorcycle according to the second preset diagnostic criterion. The second preset diagnostic criterion includes: obtaining the parameter value at the location of the second fault data and marking it as the second fault value, obtaining the second fault range through the second fault value, and constructing a second fault set; constructing a historical second diagnostic value set based on the historical data of all sensors of the motorcycle; comparing each second diagnostic value in the historical second diagnostic value set with the second fault value in the second fault set one by one, and marking the fault type of the sensors of the motorcycle through the comparison result.

6. A fault self-diagnosis method for a motorcycle electronic control system, characterized in that Including: When there is no fault in the motorcycle electronic control system, a first detection is constructed according to the working state of the ECU control unit, and operating parameter detection is performed. When a fault occurs in the motorcycle electronic control system, a second detection is constructed according to the abnormal temperature change rate, and the operating parameters of sensors and actuators related to temperature are preferentially detected. Obtain the fault diagnosis result of the ECU control unit according to the first preset diagnostic criterion; perform Laplace transform on the first diagnostic data of the first detection and the second detection, extract the periodic characteristics in the extremely cold environment to obtain the first fault data of the ECU control unit, and the first fault data includes the trigger moment, duration, and interval period. The first diagnostic criterion includes: matching and comparing the first fault data in the extremely cold state with the normal diagnostic data in the normal temperature state, and judging whether there is a fault in the ECU control unit in the extremely cold state by combining the proportion of the first diagnostic data occupied by the first detection and the second detection; comparing the similarity of the sensor signal waveforms between the extremely cold and normal temperatures, and determining it as a low-temperature specific fault when the DTW distance is greater than the preset threshold. When a fault occurs during the detection process, obtain the parameter value of the motorcycle electronic control system and mark it as the first fault value, obtain the first fault range through the first fault value, and construct a first fault set; construct a historical first diagnostic value set based on the historical data of the motorcycle electronic control system; compare each first diagnostic value in the historical first diagnostic value set with the first fault value in the first fault set one by one, and mark the fault type of the motorcycle electronic control system through the comparison result. Construct second diagnostic data based on the operating parameters collected from the first detection and the second detection, process the second diagnostic data to obtain second fault data, and obtain the motorcycle fault diagnosis result according to the second preset diagnostic criterion.

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