Expected Electrical Life Prediction of AC Contactor Based on Rough Set and Evidence Theory

被引：0

作者：

Sun S. ^{[1
]}

Wang R. ^{[1
]}

Du T. ^{[1
]}

Wang J. ^{[2
]}

Cui J. ^{[1
]}

机构：

[1] School of Artificial Intelligence, Hebei University of Technology, Tianjin

[2] State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin

来源：

Diangong Jishu Xuebao/Transactions of China Electrotechnical Society | 2020年 / 35卷 / 10期

关键词：

AC contactor; Attribute conditional density; Evidence theory; Life prediction; Rough set theory;

D O I：

10.19595/j.cnki.1000-6753.tces.190633

中图分类号：

学科分类号：

摘要：

At present, the research on the electrical life prediction of low-voltage electrical apparatus mostly focuses on the residual electrical life prediction. However, predicting the expected electrical life is more effective in guiding the use and maintenance of products and improving the reliability of the system during service period. AC contactor's initial state characteristics can reflect the length of the individual's electrical life to a certain extent, so a method for predicting the expected electrical life of AC contactor based on rough set and evidence theory was proposed. Firstly, the rough set theory and corresponding calculation were used to obtain the key initial state characteristics through twice screening. Secondly, the weight set of key initial state characteristics was established by subjective and objective comprehensive weighting method and the matching degree table of verification sample's key initial state characteristics was established based on the attribute condition density. Finally, the independent evidence was constructed by the matching degree table and weight set, then evidence theory was used to fuse evidences and decide the individual's expected electrical life grade of verification sample. The measured data proves that in the case of small samples, the method can qualitatively evaluate the expected electrical life of the AC contactor and the prediction effect is good. © 2020, Electrical Technology Press Co. Ltd. All right res

引用

页码：2158 / 2169

页数：11

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