Incipient Fault Diagnosis Method of Vehicle Power Supply Based on Feature Optimization and Deep Learning

doi:10.12382/bgxb.2021.0577

Abstract

Abstract: The vehicle power supply is the main power source for the training and emergency management of military equipment. The accurate diagnosis of incipient faults can effectively prevent the occurrence of serious faults. However, monitoring data is often redundant, making it difficult to effectively extract the symptoms of incipient faults. Aiming at this issue, a new intelligent incipient fault diagnosis method is proposed based on a combination of recursive feature elimination (RFE) and stacked auto-encoders (SAE). The collected feature variables are sorted based on their importance by using RFE to eliminate redundancy and extract the optimal subset of feature features. Then, the feature subset is used as the input of the SAE deep network, and the incipient fault category is used as the output, resulting in the effective diagnosis of incipient faults in vehicle power supply. The simulation results show that compared with SAE and shallow neural networks, the diagnosis accuracy of the proposed method is significantly improved, reaching 95.4%.

Key words: vehiclepowersupply, incipientfaultdiagnosis, recursivefeatureelimination, stackedauto-encoder

CLC Number:

LI Wei, HAN Yinlong, SUN Xiaojing. Incipient Fault Diagnosis Method of Vehicle Power Supply Based on Feature Optimization and Deep Learning[J]. Acta Armamentarii, 2022, 43(11): 2935-2944.

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