Fault pre-diagnosis system and method thereof

Abstract

The invention relates to the technical field of automobile fault diagnosis, and discloses a fault pre-diagnosis system and a method thereof.The fault pre-diagnosis system comprises a cloud platform, an electric device and a user terminal, the cloud platform is in communication connection with a fault diagnosis unit, the electric device is electrically connected with an intelligent distribution box unit, and the intelligent distribution box unit is in communication connection with the fault diagnosis unit. The cloud platform is in communication connection with the user terminal, and the output end of the intelligent distribution box unit is in signal connection with a cloud data processing unit. Through integrating data acquisition, data cleaning and filtering, data analysis, model establishment and an early warning algorithm, the method achieves the pre-judgment of potential faults of the low-voltage system, improves the reliability and safety of a whole vehicle, reduces the after-sales cost, improves the average fault-free time of the vehicle, effectively reduces the fire probability of the low-voltage system, and improves the safety of the whole vehicle. And the purpose of reducing the design cost of the low-voltage system is achieved by analyzing the data of the fault pre-diagnosis model.

Description

Technical Field

[0001] The present invention relates to the technical field of automobile fault diagnosis, and in particular to a fault pre-diagnosis system and method thereof. Background Art

[0002] In the current new energy commercial vehicle industry, the low-voltage power distribution system serves as the neural network system of the entire vehicle. Its stability and reliability are directly related to the performance and safety of the entire vehicle. With the widespread application of intelligent power distribution modules in commercial vehicles, low-voltage power distribution information has been visualized. At the same time, the development of big data and AI technology has provided a solid foundation for the fault prediction technology of low-voltage intelligent power distribution systems.

[0003] At present, during the use of new energy commercial vehicles, the failure rate of the low-voltage system of the whole vehicle is relatively high, accounting for more than 30% of the vehicle failures. In addition, the fault points are highly hidden, the troubleshooting time is long, and the maintenance is difficult. In the actual application of existing low-voltage distribution system fault pre-diagnosis technology in the new energy commercial vehicle industry, the efficiency and accuracy of fault diagnosis are not high for key electrical components with high failure rates such as on-board electronic fans and water pumps. These problems not only increase customers' operating costs and downtime, but also seriously affect customer experience. Therefore, a fault pre-diagnosis system and method are proposed here to solve the above problems. Summary of the Invention

[0004] In order to solve the above-mentioned problems, the present invention provides a fault prediction system.

[0005] The present invention provides a fault pre-diagnosis system that adopts the following technical solutions: A fault pre-diagnosis system includes a cloud platform, an electrical device, and a user terminal, wherein the cloud platform is communicatively connected to a fault diagnosis unit, the electrical device is electrically connected to an intelligent distribution box unit, and the intelligent distribution box unit is communicatively connected to the fault diagnosis unit; The cloud platform is in communication with the user terminal, and the output terminal signal of the intelligent distribution box unit is connected to the cloud data processing unit.

[0006] Preferably, the electrical device includes a fault feedback module and a PWM control module. The fault feedback module is used to upload possible fault information of the electrical device to the cloud platform, and the PWM control module can monitor the fan speed in real time and adjust it.

[0007] Preferably, the fault diagnosis unit includes a parameter estimation module, a power distribution system control module, a physical model building module and a fault simulation module.

[0008] Preferably, the parameter estimation module is used to perform algorithm estimation on the data collected by the intelligent distribution box unit, and the power distribution system control module issues power distribution control instructions to the intelligent power distribution module according to the data category.

[0009] Preferably, the physical model building module constructs a physical model of the power distribution system based on the power distribution data, and the fault simulation module simulates the fault behavior to further predict the fault and perform diagnosis.

[0010] Preferably, the cloud data processing unit includes a data cleaning module and a data integration module. The data cleaning module is used to deduplicate the collected data, process missing values, correct data errors, and process abnormal data to ensure the integrity and consistency of the data. The data integration module further classifies, integrates, and segments the data, extracts global and local features of the sensor data, and eliminates invalid data.

[0011] Preferably, the intelligent distribution box unit is responsible for collecting data such as voltage and current of the vehicle's low-voltage electrical components and sending the data to the cloud platform.

[0012] Preferably, the intelligent distribution box unit includes a data receiving module, a data storage module and a data backup module. The data storage module stores the collected data, and the data backup module further backs up the stored data to avoid loss of important data.

[0013] Preferably, the cloud platform includes a database building module, a firewall module and a data analysis module. The database building module is used to build a database and store the received data in the cloud. The firewall module scans the communication network and filters malicious attacks to ensure communication security. The data analysis module centrally manages and analyzes the collected data.

[0014] Another technical problem to be solved by the present invention is to provide a diagnostic method for a fault pre-diagnosis system, comprising the following steps: Data collection Use the intelligent distribution box unit to collect voltage, current and other data of electrical components and send them to the cloud platform in real time; Data preprocessing Perform pre-processing operations such as cleaning, alignment, and segmentation on the collected data to extract feature information useful for fault pre-diagnosis; Model training Based on the preprocessed data and combined with known fault samples, a supervised learning algorithm is used to train the fault prediction model. At the same time, an unsupervised learning algorithm is used to perform cluster analysis on unknown fault types to improve the generalization ability of the model. Real-time warning Input the real-time collected data into the trained fault pre-diagnosis model to predict and diagnose faults of electrical components. Once a potential fault is discovered, an early warning message is immediately sent to the user terminal through the cloud platform. Diagnosis result feedback Users inspect and repair vehicles based on early warning information and feed back the repair results to the cloud platform for continuous optimization and improvement of the fault prediction model.

[0015] In summary, the present invention has the following beneficial technical effects: A fault pre-diagnosis system and method thereof, which achieves early prediction of potential faults in the low-voltage system by integrating data collection, data cleaning and filtering, data analysis, model building and early warning algorithms, thereby improving the reliability and safety of the entire vehicle, reducing after-sales costs, increasing the vehicle's mean time between failures, and effectively reducing the probability of fire in the low-voltage system. By analyzing the data of the fault pre-diagnosis model, the design cost of the low-voltage system is reduced.

[0016] A fault pre-diagnosis system and method thereof, which uses data collected by intelligent distribution box units and fault pre-diagnosis models to achieve fault pre-diagnosis of key electrical components, improve customer experience, establish a fault pre-diagnosis model and early warning system, and generate an equipment health model that can be reused in other vehicle models and scenarios. By analyzing the data of the fault pre-diagnosis model, the low-voltage system design is optimized and production costs are reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a system structure diagram of the present invention; Figure 2 This is a diagram of the structure of the cloud platform in the present invention; Figure 3 This is a diagram showing the structure of a fault diagnosis unit in the present invention; Figure 4 A diagram showing the structure of an electrical device in the present invention; Figure 5 This is a diagram showing the structure of the intelligent distribution box unit in the present invention; Figure 6 This is a diagram showing the structure of the cloud data processing unit in the present invention; Figure 7 is a fault diagnosis flow chart of the present invention; Figure 8 This is a schematic diagram of the model training of the present invention; Figure 9 It is a system scheme diagram of the present invention; Figure 10 This is an illustrative diagram of the present invention. DETAILED DESCRIPTION

[0018] The following is combined with Figure 1-10The present invention is described in further detail.

[0019] The present invention discloses a fault pre-diagnosis system. Figure 1-9 A fault pre-diagnosis system includes a cloud platform, an electrical device, and a user terminal. The cloud platform is communicatively connected to a fault diagnosis unit, the electrical device is electrically connected to an intelligent power distribution box unit, the intelligent power distribution box unit is communicatively connected to the fault diagnosis unit, the cloud platform is communicatively connected to the user terminal, and the output signal of the intelligent power distribution box unit is connected to a cloud data processing unit. The electrical device includes a fault feedback module and a PWM control module. The fault feedback module is used to upload possible fault information of the electrical device to the cloud platform. The PWM control module can monitor the fan speed in real time and adjust it. The fault diagnosis unit includes a parameter estimation module, a distribution system control module, a physical model building module and a fault simulation module. The parameter estimation module is used to perform algorithm estimation on the data collected by the intelligent distribution box unit. The distribution system control module issues distribution control instructions to the intelligent distribution and modules according to the data category. The physical model building module builds a physical model of the distribution system for the distribution data. The fault simulation module simulates the fault behavior to further predict faults and perform diagnosis. The cloud data processing unit includes a data cleaning module and a data integration module. The data cleaning module is used to deduplicate the collected data, process missing values, correct data errors and process abnormal data to ensure the integrity and consistency of the data. The data integration module further classifies, integrates and segments the data, extracts the global and local features of the sensor data, and eliminates invalid data.

[0020] The intelligent distribution box unit is responsible for collecting data such as voltage and current of the vehicle's low-voltage electrical components and sending the data to the cloud platform. The intelligent distribution box unit includes a data receiving module, a data storage module and a data backup module. The data storage module stores the collected data, and the data backup module further backs up the stored data to avoid the loss of important data. The cloud platform includes a database construction module, a firewall module and a data analysis module. The database construction module is used to build a database and store the received data in the cloud. The firewall module scans the communication network and filters malicious attacks to ensure communication security. The data analysis module centrally manages and analyzes the collected data. By integrating data collection, data cleaning and filtering, data analysis, model building and early warning algorithms, it can achieve early prediction of potential faults in the low-voltage system, thereby improving the reliability and safety of the entire vehicle, reducing after-sales costs, and increasing the vehicle's average failure-free time.

[0021] Another technical problem to be solved by the present invention is to provide a diagnostic method for a fault pre-diagnosis system, comprising the following steps: Data collection Use the intelligent distribution box unit to collect voltage, current and other data of electrical components and send them to the cloud platform in real time; Data preprocessing Perform pre-processing operations such as cleaning, alignment, and segmentation on the collected data to extract feature information useful for fault pre-diagnosis; Model training Based on the preprocessed data and combined with known fault samples, a supervised learning algorithm is used to train the fault prediction model. At the same time, an unsupervised learning algorithm is used to perform cluster analysis on unknown fault types to improve the generalization ability of the model. Real-time warning Input the real-time collected data into the trained fault pre-diagnosis model to predict and diagnose faults of electrical components. Once a potential fault is discovered, an early warning message is immediately sent to the user terminal through the cloud platform. Diagnosis result feedback Users inspect and repair vehicles based on early warning information and feed back the repair results to the cloud platform for continuous optimization and improvement of the fault prediction model.

[0022] By integrating data collection, data cleaning and filtering, data analysis, model building and early warning algorithms, potential faults in the low-voltage system can be predicted in advance, thereby improving the reliability and safety of the entire vehicle, reducing after-sales costs, increasing the vehicle's mean time between failures, and effectively reducing the probability of fire in the low-voltage system. By analyzing the data of the fault prediction model, the design cost of the low-voltage system can be reduced.

[0023] Reference Figure 10 The present invention performs effective fault pre-diagnosis on electrical components. The electrical components can be key electrical components such as electronic fans and water pumps. Taking electronic fans with a high failure rate as an example, accurate fault pre-diagnosis can effectively improve the safety and reliability of electronic fans, enhance customer experience, establish a fault pre-diagnosis model and early warning system, and generate an equipment health model that can be reused in other vehicle models and scenarios. By analyzing the data of the fault pre-diagnosis model, the low-voltage system design is optimized and the production cost is reduced.

[0024] The above are all preferred embodiments of the present invention, and are not intended to limit the scope of protection of the present invention. Therefore, any equivalent changes made based on the structure, shape, and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A fault pre-diagnosis system, comprising a cloud platform, electrical components, and user terminals, characterized in that: The cloud platform is communicatively connected to a fault diagnosis unit, the electrical device is electrically connected to an intelligent power distribution box unit, and the intelligent power distribution box unit is communicatively connected to the fault diagnosis unit; The cloud platform is in communication with the user terminal, and the output terminal signal of the intelligent distribution box unit is connected to the cloud data processing unit.

2. A fault prediction system according to claim 1, characterized in that: The electrical device includes a fault feedback module and a PWM control module. The fault feedback module is used to upload possible fault information of the electrical device to the cloud platform, and the PWM control module can monitor the fan speed in real time and adjust it.

3. A fault prediction system according to claim 1, characterized in that: The fault diagnosis unit includes a parameter estimation module, a power distribution system control module, a physical model establishment module and a fault simulation module.

4. A fault prediction system according to claim 3, characterized in that: The parameter estimation module is used to perform algorithm estimation on the data collected by the intelligent distribution box unit, and the distribution system control module issues distribution control instructions to the intelligent distribution box module according to the data category.

5. A fault prediction system according to claim 3, characterized in that: The physical model building module constructs a physical model of the power distribution system based on the power distribution data, and the fault simulation module simulates the fault behavior to further predict the fault and perform diagnosis.

6. A fault prediction system according to claim 1, characterized in that: The cloud data processing unit includes a data cleaning module and a data integration module. The data cleaning module is used to remove duplicates, process missing values, correct data errors, and process abnormal data to ensure the integrity and consistency of the data. The data integration module further classifies, integrates, and segments the data, extracts global and local features of sensor data, and eliminates invalid data.

7. The fault prediction system according to claim 1, characterized in that: The intelligent distribution box unit is responsible for collecting data such as voltage and current of the vehicle's low-voltage electrical components and sending the data to the cloud platform.

8. The fault prediction system according to claim 1, characterized in that: The intelligent distribution box unit includes a data receiving module, a data storage module and a data backup module. The data storage module stores the collected data, and the data backup module further backs up the stored data to avoid loss of important data.

9. The fault prediction system according to claim 1, characterized in that: The cloud platform includes a database construction module, a firewall module and a data analysis module. The database construction module is used to build a database and store the received data in the cloud. The firewall module scans the communication network and filters malicious attacks to ensure communication security. The data analysis module centrally manages and analyzes the collected data.

10. A diagnostic method for a fault pre-diagnosis system according to any one of claims 1 to 9, characterized in that: The following steps are involved: Data collection Use the intelligent distribution box unit to collect voltage, current and other data of electrical components and send them to the cloud platform in real time; Data preprocessing Perform pre-processing operations such as cleaning, alignment, and segmentation on the collected data to extract feature information useful for fault pre-diagnosis; Model training Based on the preprocessed data and combined with known fault samples, a supervised learning algorithm is used to train the fault prediction model. At the same time, an unsupervised learning algorithm is used to perform cluster analysis on unknown fault types to improve the generalization ability of the model. Real-time warning Input the real-time collected data into the trained fault pre-diagnosis model to predict and diagnose faults of electrical components. Once a potential fault is discovered, an early warning message is immediately sent to the user terminal through the cloud platform. Diagnosis result feedback Users inspect and repair vehicles based on early warning information and feed back the repair results to the cloud platform for continuous optimization and improvement of the fault prediction model.

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