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

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

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.

Key words: long short-time memory network, sequential probability ratio test, vehicle power supply simulation system, fault diagnosis

CLC Number:

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.

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