Research on DC-DC power health management technology based on degradation test and deep learning. Issue 1 (November 2021)

• Record Type: Journal Article
• Authors: Shi, Linlin; Yu, Pengfei; He, Shilie; Zhou, Zhenwei; Meng, Linghui; Liu, Junbin; Cheng, Liye; Huang, Yun
• Is Part Of: IOP conference series. Volume 1207:Issue 1(2021)
• Publication Date: 2021-11
• Subjects: degradation -- DC-DC -- Health Management -- deep learning.; Materials science -- Periodicals; 620.1105
• DOI: 10.1088/1757-899X/1207/1/012005
• Languages: English
• ISSNs: 1757-8981

Abstract

Abstract: The health state of DC-DC power supply is the key factor to determine whether the electronic equipment can operate normally. The failure and deterioration of the power supply system will lead to the collapse of the entire electronic system. The research in this paper is based on the long-term high temperature degradation test data of a certain type of DC-DC power supply. The degradation law of power supply is studied by data preprocessing and noise reduction of sensitive parameters such as input current, output current, input voltage and output voltage. On this basis, we use the method of deep learning to model the efficiency of power supply in the process of degradation test. The experimental results show that the efficiency time series modeling of power supply degradation using LSTM method can effectively reflect the law of power supply efficiency degradation. Based on the DC-DC power health management technology combining degradation test and deep learning, the advanced fault prediction model is used to reflect the change law of power supply in the real degradation process. This method has certain theoretical and engineering value for power PHM modeling and application.