Fault self-diagnosis system and method for electric control system of motorcycle

Abstract

The invention relates to the technical field of electric control fault diagnosis, in particular to a fault self-diagnosis system and method for a motorcycle electric control system, and the system comprises a data collection module which carries out the data detection according to the motorcycle electric control system; the first fault diagnosis module constructs first diagnosis data according to the detection trigger frequency, duration and execution data, processes the first diagnosis data to obtain first fault data, and obtains a fault diagnosis result of the ECU according to a first preset diagnosis standard; and the second fault diagnosis module constructs second diagnosis data according to the operation parameters acquired by the first detection and the second detection, processes the second diagnosis data to obtain second fault data, and obtains a motorcycle fault diagnosis result according to a second preset diagnosis standard.

Description

Technical Field

[0001] The present invention relates to the technical field of electronic control fault diagnosis, and particularly 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 diagnostic methods often rely on manual experience and point-by-point troubleshooting, resulting in low diagnostic 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 diagnostic means, it is difficult to share data between systems, and it is difficult to achieve remote collaborative diagnosis, further exacerbating the difficulty of problem troubleshooting. On the other hand, current systems often lack intelligent means and do not integrate expert systems or AI-assisted functions, resulting in insufficient judgment ability for complex or latent faults.

[0003] Due to the lack of a dynamic monitoring mechanism, the real-time performance of the system is poor, and faults are often not captured quickly after they occur, easily leading to further equipment damage or safety hazards. Due to the large differences in motorcycle models, the data standards of electronic control systems are not unified, further increasing the development difficulty of the diagnostic system. The above problems jointly restrict the efficiency and accuracy of fault diagnosis of motorcycle electronic control systems, and 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 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 second fault diagnosis module constructs second diagnostic data based on the operating 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 standard.

[0006] To achieve the above object, the present invention provides the following technical solutions: A fault self-diagnosis system for a motorcycle electronic control system, comprising: 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. Further, the system further includes a data storage module, which is used to store the collected data. The collected 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. 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.

[0007] Further, 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, 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. 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.

[0008] Further, the second detection includes: Under extremely cold conditions, increase the monitoring of the ambient temperature change rate in the sensor area. When the ambient temperature change rate exceeds the threshold, even if other parameters do not reach the conditions for triggering the second detection, the passive detection is also forced to be triggered. 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.

[0009] 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. Further, the first fault diagnosis module includes: When no faults occur 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. 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 in 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 diagnostic value set according to the historical data of the motorcycle electronic control system; compare the first diagnostic values 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 results.

[0010] The second fault diagnosis module constructs second diagnostic data based on the operating 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.

[0011] Furthermore, the second fault diagnosis module includes: The second fault diagnosis module constructs 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 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 values at the position where the second fault data is located and marking them as the second fault values, obtaining the second fault range through the second fault values, and constructing a second fault set; constructing a historical second diagnostic value set according to the historical data of all the sensors of the motorcycle; comparing the second diagnostic values 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 sensors of the motorcycle according to the comparison results.

[0012] The present invention also provides a fault self-diagnosis method for a motorcycle electronic control system, including: When the motorcycle electronic control system does not have a fault, construct a first detection according to the working state of the ECU control unit and perform operating parameter detection; when the motorcycle electronic control system has a fault, construct a second detection according to the abnormal temperature change rate and preferentially detect the operating parameters of the sensors and actuators related to the temperature; Construct first diagnostic data according to the detection trigger frequency, duration, and execution data, process the first diagnostic data to obtain first fault data, and obtain the fault diagnosis result of the ECU control unit according to the first preset diagnostic criterion; Construct the second diagnostic data based on 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.

[0013] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. After evaluating the ECU load and working state, dynamically calculate the maximum allowable trigger frequency according to the ambient temperature, and perform active detection only when the ECU is in a low-load and stable state, thus effectively avoiding errors introduced by high load or instability during startup; while in case of a fault or extremely cold conditions, passively trigger detection by the abnormal temperature change rate, and prioritize the sensors according to the temperature sensitivity, which can quickly capture abnormal situations related to temperature; through the combination of the first detection and the second detection, intelligent adaptive monitoring in different operating states is realized.

[0014] 2. By performing Laplace transform on the first diagnostic data, extract the periodic characteristics under extremely cold conditions, and compare the similarity of the characteristics with the normal data at normal temperature by combining 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.

[0015] 3. Through comprehensive analysis and data cleaning processing of the operating parameters collected by the first detection and the second detection, construct accurate second diagnostic data, and use the second preset diagnostic criterion to compare with the historical diagnostic data, so as to be able to more accurately locate the fault areas of the 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 time, reduces the maintenance cost, and ensures the stable operation of the motorcycle electronic control system under various working conditions. Description of the Drawings

[0016] Figure 1 It is a schematic structural diagram of a fault self-diagnosis system for a motorcycle electronic control system provided by an embodiment of the present invention; Figure 2 It is a working flow chart of the first fault diagnosis module provided by an embodiment of the present invention; Figure 3 It is a flow chart of a fault self-diagnosis method for a motorcycle electronic control system provided by an embodiment of the present invention. Detailed Embodiments

[0017] 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. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention. Embodiment 1:

[0018] 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 the 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.

[0019] A certain company introduced a fault self-diagnosis system for a motorcycle electronic control system provided by the present invention to detect whether there are faults in the motorcycle electronic control system at extremely cold temperatures, so as to improve the real-time monitoring accuracy and efficiency of the motorcycle electronic control system at cold temperatures. The system structure is as Figure 1 shown, and the specific implementation method is as follows: When the motorcycle electronic control system does not have a fault, the data acquisition module constructs a first detection according to the working state of the ECU control unit and performs operation parameter detection; when the motorcycle electronic control system has a fault, 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; Furthermore, the data storage module is used to store the collected data. The collected 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, the first detection execution result, and also includes the second detection trigger frequency, the second detection response duration, the second detection duration, the second detection execution result, etc.; 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.

[0020] Further, the detection trigger frequency represents the frequency at which the electronic control system detects data of 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 a 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.

[0021] Further, the second diagnostic data represents the specific data collected by the first and second detections, including not only fork data, suspension data, braking data, etc., but also data of related components collected by actuators and sensors, including data of oxygen sensors, crankshaft sensors, electronic throttles, etc.

[0022] Record the operating states of key components of the motorcycle in real time, which can not only capture dynamic data such as the response duration, trigger frequency, and detection duration between sensors and actuators in a timely manner, but also record the specific data of related components such as forks, suspensions, brakes, oxygen sensors, crankshaft sensors, and electronic throttles. 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.

[0023] Further, 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 (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. 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.

[0024] Further, construct a load score to measure the load state. The formula for the load score is: ; Wherein, represents the load score, represents the CPU load is the weight coefficient of represents the memory occupancy rate is the weight coefficient of Represents the bus load The weight coefficient of Represents the junction temperature of the ECU chip The weight coefficient of Represents the maximum allowable junction temperature.

[0025] Table 1. Operating status of the electronic control system

[0026] Furthermore, if the load score is greater than the preset threshold, the first detection is delayed. As shown in Table 1, the load scores detected at some times can be found that the electronic control system is in a high-load stage, and the first detection is delayed.

[0027] Furthermore, if the load score is not greater than the preset threshold, the first detection is performed. According to the current ambient temperature and the operating status of the ECU, the maximum allowable trigger frequency is calculated. The formula is: ; Wherein Represents the maximum allowable trigger frequency Represents the basic detection frequency Represents the temperature attenuation coefficient Represents the junction temperature of the ECU chip 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.

[0028] Furthermore, the trigger value of the first detection is the preset threshold of 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.

[0029] Through the above content, intelligent adaptive detection can be achieved. Without interfering with the key tasks of the ECU, it ensures that the fault detection is triggered only when the system load is low and the temperature is appropriate, avoiding errors and data noise caused by detection under high load or high temperature, and reducing 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 ensures that the detection is carried out at the optimal time, thereby improving the accuracy and response efficiency of fault detection, while extending the service life of the ECU and related components, and enhancing the overall safety and stability of the motorcycle.

[0030] Furthermore, the second detection includes: Under extremely cold conditions, monitor the rate of change of the ambient temperature in the sensor area; When the rate of change of the ambient temperature exceeds the threshold, passive detection is forced to trigger even if other parameters do not meet the second detection condition; After passive detection is triggered, sort according to the sensitivity of the sensors to temperature, and preferentially detect sensors and actuators related to temperature.

[0031] Furthermore, collect the temperatures of sensors in multiple regions, including ambient temperature, engine compartment, battery compartment, etc., collect real-time data, and calculate the local rate of change of temperature in this region. In this embodiment, the local rate of change of temperature represents the sensitivity of the sensor to temperature; Furthermore, after calculating the local rates of change of temperature in all regions, sort according to the absolute value of the local rate of change of temperature, preferentially detect sensors and actuators related to temperature, and obtain all data in this region, including the operating data and fault data of the motorcycle, such as coolant temperature sensors, fuel heaters, etc., because these components are more likely to fail under extremely cold conditions.

[0032] Through the real-time monitoring of the rates of change of the temperatures of sensors in multiple regions and the method of sorting and detecting sensor data according to the rate of change of temperature, 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; sorting and detecting according to the sensitivity to temperature preferentially guarantees the diagnosis of vulnerable components such as coolant sensors, fuel heaters, and brake pads, greatly improving the fault diagnosis rate of key parts.

[0033] 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 working process of the first fault diagnosis module is as Figure 2 shown; Furthermore, the first fault diagnosis module includes: when no fault occurs during the detection process, perform Laplace transform on the first diagnosis 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, duration, and interval period; The first diagnosis standard includes: matching and comparing the first fault data in the extremely cold state with the normal diagnosis data in the normal temperature state, and combining the proportion of the first diagnosis 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 in extremely cold and normal temperatures, and determining 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 based on the first fault values, and construct the first fault set; construct the 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 results.

[0034] Furthermore, the first detection and the second detection of the motorcycle electronic control system have triggering rules. The triggering rules of the first detection and the second detection can be extracted through Laplace transform to obtain the first fault data similar to the frequency band. Furthermore, 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 indicates 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 room temperature, and then perform waveform matching according to 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 too low temperature will cause the electronic control system to fail to work properly.

[0035] Furthermore, collect all the parameter values of the motorcycle electronic control system and mark them as the first fault values, and then compare them 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.

[0036] Table 2. Fault Judgment Results of Motorcycle Electronic Control System

[0037] The first fault diagnosis module analyzes the trigger frequencies, execution durations, and data characteristics of the first detection and the second detection, constructs first diagnostic data including trigger moments, durations, and interval periods, performs Laplace transform on it to extract periodic characteristics in extremely cold environments, and dynamically evaluates the system health status in combination with the proportion of the second detection data. 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 similarities of the sensor signals in 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 real-time collects all parameters of the electronic control system and marks them as first fault values, and through 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 gaps are identified based on the degree of difference, so as to effectively distinguish transient anomalies caused by extreme cold from permanent hardware damage, and ensure that the diagnosis results cover both environmental adaptability and compound faults of the system's inherent defects.

[0038] The second fault diagnosis module constructs second diagnostic data based on the operating 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 standard.

[0039] Further, the second fault diagnosis module includes: The second fault diagnosis module constructs 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 second fault data, and obtains the motorcycle fault diagnosis result according to the second preset diagnostic standard; The second preset diagnostic standard includes: obtaining the parameter value at the position where the second fault data is located 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 diagnosis value set based on the historical data of all sensors of the motorcycle; comparing each second diagnosis value in the historical second diagnosis value set with the second fault value in the second fault set one by one, and marking the fault types of the sensors of the motorcycle through the comparison results.

[0040] 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, and electronic throttles, etc., and contains specific data collected by various sensors on the motorcycle.

[0041] 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 to obtain the specific fault types existing in the sensors of the motorcycle. As shown in Table 3, these are the possible locations where the sensors of the motorcycle may have faults.

[0042] Table 3. Fault Judgment Results of the Sensors of the Motorcycle

[0043] The second fault diagnosis module performs data cleaning, noise reduction, and smoothing 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 eliminating environmental interference and signal distortion. Combining with the fault mode matching of the historical second diagnostic value set, it can accurately identify the abnormal states of sensors and actuators, reduce the risks of misjudgment and missed detection, and support preventive maintenance decisions, ultimately achieving high reliability and long-life operation of the motorcycle electronic control system under extreme working conditions.

[0044] The system adopts a hierarchical diagnosis mechanism. When there is no fault, it actively captures periodic characteristics through the state of the ECU control unit to drive the first detection. When the abnormal temperature change rate exceeds the threshold, it forcibly triggers the second detection and preferentially scans temperature-sensitive components, extracts the abnormal fluctuation law in the low-temperature environment by combining the Laplace transform, and compares the signal waveform differences between extremely cold and normal temperatures through the dynamic time warping (DTW) algorithm to accurately identify low-temperature-specific faults; at the same time, it constructs a diagnostic value set based on historical data, judges the fault values of the collected data, accurately locates the positions where faults exist, reduces the fault misjudgment rate, and shortens the diagnostic response time. Embodiment 2:

[0045] 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 is as follows: 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 sensors and actuators related to temperature; Further, the collected data from the first detection and the second detection also need to be stored. The collected 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, 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, the second detection execution result, etc. of the ECU control unit; 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.

[0046] Further, 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 process.

[0047] Table 4. Working State of the Electronic Control System

[0048] Further, according to the current ambient temperature and the working state of the ECU, the dynamic maximum allowable trigger frequency is calculated; Further, the first detection is performed according to the maximum allowable trigger frequency and the trigger threshold of the first detection.

[0049] Further, the second detection includes: Under extremely cold conditions, the monitoring of the ambient temperature change rate in the sensor area is increased; 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; 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.

[0050] 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. According to the first preset diagnostic criteria, the fault diagnosis result of the ECU control unit is obtained; Further, when no fault occurs during the detection process, the Laplace transform is performed on the first diagnostic data of the first detection and the second detection, and the periodic characteristics in the extremely cold environment are extracted to obtain the first fault data of the ECU control unit. The first fault data includes the trigger moment, duration, and interval period; The first diagnostic criteria include: 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 of 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 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; 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 diagnosis value set according to the historical data of the motorcycle electronic control system; compare the first diagnosis values 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 results.

[0051] Construct the second diagnosis data according to the operating parameters collected by the first detection and the second detection, process the second diagnosis data to obtain the second fault data, and obtain the motorcycle fault diagnosis result according to the second preset diagnosis standard.

[0052] Furthermore, construct the second diagnosis data according to the operating parameters collected by the first detection and the second detection, perform data cleaning and processing on the second diagnosis data to obtain the second fault data, and obtain the fault diagnosis result of the motorcycle according to the second preset diagnosis standard; The second preset diagnosis standard includes: obtaining the parameter values at the position where the second fault data is located 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 diagnosis value set according to the historical data of all the 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 type of the sensors of the motorcycle according to the comparison results. Table 5 shows the sensors of the motorcycle that may have faults diagnosed under extremely cold conditions.

[0053] Table 5. Diagnostic results of some motorcycle sensors

[0054] 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 realized, and the fault location of the electronic control system can be accurately judged when a fault occurs, improving the fault detection efficiency.

[0055] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and 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: include: 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 an operating parameter detection; When a motorcycle electronic control system fails, the data acquisition module constructs a second detection based on the abnormal temperature change rate, and gives priority to detecting the operating parameters of sensors and actuators related to temperature; The first fault diagnosis module constructs first diagnostic data according to the detection trigger frequency, duration, and execution data, processes the first diagnostic data to obtain first fault data, and obtains a fault diagnosis result of the ECU control unit according to a first preset diagnostic standard; The second fault diagnosis module constructs second diagnostic data according to the operating parameters collected by the first detection and the second detection, processes the second diagnostic data to obtain second fault data, and obtains a motorcycle fault diagnosis result according to a second preset diagnostic standard.

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

3. A fault self-diagnosis system for a motorcycle electronic control system according to claim 1, characterized in that: The first test includes: The first detection is the data of the motorcycle actively detected by the electronic control system. Before the first detection is triggered, the current load and working status of the ECU control unit are evaluated. If the ECU is under high load or unstable state, the triggering of active detection is delayed until the load score of the ECU load is no greater than the preset threshold. Calculate the dynamic maximum allowable trigger frequency based on the current ambient temperature and the working status of the ECU; The first detection is performed according to the maximum allowed trigger frequency and the trigger threshold of the first detection.

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

5. The fault self-diagnosis system of a motorcycle electronic control system according to claim 1, characterized in that: The first fault diagnosis module includes: When no fault occurs during the detection process, Laplace transform is performed on the first diagnostic data of the first detection and the second detection to extract the periodic characteristics under the extreme cold environment, and obtain the first fault data of the ECU control unit, the first fault data including the triggering time, duration, and interval period; The first diagnostic standard includes: matching and comparing the first fault data in the extreme cold state with the normal diagnostic data in the normal temperature state, combining the proportion of the first diagnostic data of the first detection and the second detection, to determine whether the ECU control unit in the extreme cold state has a fault; comparing the similarity of the sensor signal waveforms in the extreme cold and normal temperature, and determining it as a low temperature-specific fault when the DTW distance is greater than a preset threshold; When a fault occurs during the detection process, a parameter value of the motorcycle's electronic control system is obtained and marked as a first fault value, a first fault range is obtained through the first fault value, and a first fault set is constructed; a historical first diagnostic value set is constructed based on historical data of the motorcycle's electronic control system; the first diagnostic values ​​in the historical first diagnostic value set are compared one by one with the first fault values ​​in the first fault set, and the fault type of the motorcycle's electronic control system is marked according to the comparison result.

6. A fault self-diagnosis system for 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 according to the operating parameters collected by the first detection and the second detection, cleans and processes the second diagnostic data to obtain second fault data, and obtains a fault diagnosis result of the motorcycle according to a second preset diagnostic standard; The second preset diagnostic standard includes: obtaining the parameter value of the location where the second fault data is located 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 the second diagnostic values ​​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's sensor through the comparison result.

7. A method for self-diagnosis of faults in a motorcycle electronic control system, characterized in that: include: When there is no fault in the motorcycle electronic control system, a first test is constructed according to the working state of the ECU control unit, and an operating parameter test is performed; When a motorcycle electronic control system fails, a second detection is constructed based on the abnormal temperature change rate, and the operating parameters of sensors and actuators related to temperature are detected first; Constructing first diagnostic data according to 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; Second diagnostic data is constructed according to the operating parameters collected by the first detection and the second detection, the second diagnostic data is processed to obtain second fault data, and a motorcycle fault diagnosis result is obtained according to a second preset diagnostic standard.

Images