Fault Diagnosis Method of Vehicle Power Supply Based on Deep Learning and Sequential Test

Authors

Li Wei, 1. College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China;;2. Key Laboratory of Gansu Advanced Control for Industrial Processes, Lanzhou University of Technology, Lanzhou 730050, China;;3. National Demonstration Center for Experimental Electrical and Control Engineering Education, Lanzhou University of Technology, Lanzhou 730050, China;
Bingxiang Zhou, 1. College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China;;2. Key Laboratory of Gansu Advanced Control for Industrial Processes, Lanzhou University of Technology, Lanzhou 730050, China;;3. National Demonstration Center for Experimental Electrical and Control Engineering Education, Lanzhou University of Technology, Lanzhou 730050, China;
Dongnian Jiang, 1. College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China;;2. Key Laboratory of Gansu Advanced Control for Industrial Processes, Lanzhou University of Technology, Lanzhou 730050, China;;3. National Demonstration Center for Experimental Electrical and Control Engineering Education, Lanzhou University of Technology, Lanzhou 730050, China;

Abstract

Abstract: Focus on the health maintenance of vehicle power supply, a fault diagnosis method of vehicle power supply is proposed, which is based on the long and short time memory LSTM(Long Short Time Memory) network and the sequential probability ratio test SPRT(Sequential Probability Ratio Test). Based on the LSTM network, the multivariate time series model of vehicle power supply is established, and the SPRT method is used to perform the adaptive multi-sample fault diagnosis. The experiment on the vehicle power supply simulation system shows that the LSTM diagnosis model has stronger learning and mapping capabilities, and the fault diagnosis method based on the LSTM-SPRT fusion significantly improves the accuracy and reliability of the vehicle power supply fault diagnosis.

Recommended Citation

Wei, Li; Zhou, Bingxiang; and Jiang, Dongnian (2020) "Fault Diagnosis Method of Vehicle Power Supply Based on Deep Learning and Sequential Test," Journal of System Simulation: Vol. 32: Iss. 4, Article 11.
DOI: 10.16182/j.issn1004731x.joss.18-0288
Available at: https://dc-china-simulation.researchcommons.org/journal/vol32/iss4/11

First Page

638

Revised Date

2018-09-05

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.18-0288

Last Page

648

CLC

TP391.9;TP277

Recommended Citation

Li Wei, Zhou Bingxiang, Jiang Dongnian. Fault Diagnosis Method of Vehicle Power Supply Based on Deep Learning and Sequential Test[J]. Journal of System Simulation, 2020, 32(4): 638-648.

DOI

10.16182/j.issn1004731x.joss.18-0288