Method and system for fault diagnoses of intelligent vehicles

Patent metadata

Field	Value
Publication number	US-11780452-B2
Application number	US-202117195617-A
Country	US
Kind code	B2
Filing date	Mar 8, 2021
Priority date	Oct 21, 2020
Publication date	Oct 10, 2023
Grant date	Oct 10, 2023

Abstract

A model of a system of an intelligent vehicle is trained and optimized using system operation data of the intelligent vehicle in a normal running state. The system operation data of the intelligent vehicle in a running state is collected in real time. Sensor data of the system operation data is de-noised, and feature extraction and screening are performed for a fatal sensor fault to reconstruct the system operation data. The reconstructed system operation data is inputted into the trained model to output system state data of the intelligent vehicle in the running state. The system state data is compared with a set threshold. If the system state data exceeds the set threshold, an actuator corresponding to the system state data is determined to have a fault. In addition, a system for a fault diagnosis of the intelligent vehicle is further provided.

First claim

What is claimed is:

1. A method for a fault diagnosis of an intelligent vehicle, comprising:
1) establishing a model of a system of the intelligent vehicle; acquiring system operation data of the intelligent vehicle in a normal running state; training and optimizing the model using the system operation data of the intelligent vehicle in the normal running state; wherein, before training and optimizing the model, the sensor data of the system operation data of the intelligent vehicle in the normal running state is de-noised, and feature extraction and screening are performed for a fatal sensor fault of the system operation data of the intelligent vehicle in the normal running state;
2) collecting system operation data of the intelligent vehicle in a running state in real time; de-noising sensor data of the system operation data of the intelligent vehicle in the running state, and performing feature extraction and screening for a fatal sensor fault to reconstruct the system operation data of the intelligent vehicle in the running state; inputting the reconstructed system operation data into the trained model to output system state data of the intelligent vehicle in the running state; comparing the system state data with a set threshold; and if the system state data exceeds the set threshold, determining that an actuator corresponding to the system state data has a fault, thereby completing the fault diagnosis of the intelligent vehicle;
wherein in step 1) and in step 2), the fatal sensor fault is determined according to formulas (6) and (7):
th BM <abs(Σ i=k-(W-1) k d i )  (6),

th J >Σ i=k-W J k abs( x ( k )− x ( i ))  (7),

wherein, th BM and th J are two set thresholds and are set to be 3 and 1×10 −6 respectively; W and W j are sizes of two sliding windows and are set to be 100 and 50 respectively; d i represents a three-level detail coefficient obtained through DWT at moment i; and x(k) represents the sensor data at moment k.

2. The method of claim 1 , wherein the system operation data of the intelligent vehicle in the normal running state is obtained through a storage medium of a control system of the intelligent vehicle; and a useful field in the system operation data is extracted, and data cleaning and data transformation are carried out for the useful field.

3. The method of claim 2 , wherein during the data cleaning, incomplete records are directly removed, and duplicate records are merged into one piece; the data transformation is to carry out mathematical transformation for a directly extracted field to obtain required verification information.

4. The method of claim 1 , wherein the feature extraction is performed for the system operation data of the intelligent vehicle in the normal running state at different scales using discrete wavelet transform (DWT); a signal from extracted features are reconstructed using an approximation coefficient and a detail coefficient to obtain de-noised operation data; and a signal of the system operation data of the intelligent vehicle in the normal running state is reconstructed using a threshold method.

5. The method of claim 1 , wherein the feature extraction is performed for the system operation data of the intelligent vehicle in the normal running state at different scales using DWT, and a sliding window method is adopted for the DWT.

6. The method of claim 1 , wherein the model comprises a plurality of subsystem models, and each of the subsystem models corresponds to an independent actuator.

Assignees

• Changan Univ

Inventors

• Zhao Xiangmo
• Min Haigen
• Fang Yukun
• Wu Xia
• Xu Zhigang
• Wang Runmin
• Liu Zhanwen
• Gong Siyuan
• Zhu Yu
• Wang Wuqi
• Cheng Chaoyi
• Sun Pengpeng
• Wang Zhen
• Jiang Yuande

Classifications

• B60W50/0205 Primary

  Diagnosing or detecting failures; Failure detection models · CPC title

• B60W60/001

  Planning or execution of driving tasks · CPC title

• G01S19/20

  Integrity monitoring, fault detection or fault isolation of space segment · CPC title

• G05B23/0243 Primary

  model based detection method, e.g. first-principles knowledge model · CPC title

• B60W60/00 Primary

  Drive control systems specially adapted for autonomous road vehicles · CPC title