US 11,780,452 B2
Method and system for fault diagnoses of intelligent vehicles
Xiangmo Zhao, ShaanXi (CN); Haigen Min, ShaanXi (CN); Yukun Fang, ShaanXi (CN); Xia Wu, ShaanXi (CN); Zhigang Xu, ShaanXi (CN); Runmin Wang, ShaanXi (CN); Zhanwen Liu, ShaanXi (CN); Siyuan Gong, ShaanXi (CN); Yu Zhu, ShaanXi (CN); Wuqi Wang, ShaanXi (CN); Chaoyi Cheng, ShaanXi (CN); Pengpeng Sun, ShaanXi (CN); Zhen Wang, ShaanXi (CN); and Yuande Jiang, ShaanXi (CN)
Assigned to CHANG'AN UNIVERSITY, Xi'an (CN)
Filed by CHANG'AN UNIVERSITY, ShaanXi (CN)
Filed on Mar. 8, 2021, as Appl. No. 17/195,617.
Claims priority of application No. 202011134946.9 (CN), filed on Oct. 21, 2020.
Prior Publication US 2022/0118987 A1, Apr. 21, 2022
Int. Cl. B60W 50/02 (2012.01); B60W 60/00 (2020.01); G01S 19/20 (2010.01)
CPC B60W 50/0205 (2013.01) [B60W 60/001 (2020.02); G01S 19/20 (2013.01)] 6 Claims
OG exemplary drawing
 
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):
thBM<abs(Σi=k-(W-1)kdi)  (6),
thJ>Σi=k-WJk abs(x(k)−x(i))  (7),
wherein, thBM and thJ are two set thresholds and are set to be 3 and 1×10−6 respectively; W and Wj are sizes of two sliding windows and are set to be 100 and 50 respectively; di represents a three-level detail coefficient obtained through DWT at moment i; and x(k) represents the sensor data at moment k.