Mechanical Fault Fusion Diagnosis of High Voltage Circuit Breaker Using Multi-Vibration Information Based on Random Forest

被引：0
|
作者：
Ma S. [1 ]
Wu J. [1 ]
Yuan Y. [1 ]
Jia B. [1 ]
Luo X. [1 ]
Li W. [1 ]
机构：
[1] School of Automation Science and Electrical Engineering, Beihang University, Beijing
来源：
Diangong Jishu Xuebao/Transactions of China Electrotechnical Society | 2020年 / 35卷
关键词：
Fault diagnosis; High-voltage circuit breakers (HVCB); Multi-sensor information fusion; Random forest; Vibration signal;
D O I：
10.19595/j.cnki.1000-6753.tces.191701
中图分类号：
学科分类号：
摘要：
Healthy condition of high voltage circuit breaker (HVCB) has an important impact on the power system. With the development of artificial intelligence, many advanced methods have been applied to fault type identification of HVCBS. At present, most related researches are devoted to improving the feature extraction process or the classification method based on a single sensor to obtain a higher accuracy. However, the improved method can only approach the upper limit determined by data information, ignoring the limited ability of a single information to identify faults. Therefore, this study has proposed a multi-sensor joint decision approach based on random forest. Firstly, under the typical faults condition, the differences of vibration characteristic at the different locations are analyzed. Then, based on a random forest algorithm, a multi-sensor fusion diagnosis process is designed. Finally, based on the HVCB experimental platform, the results of six typical classifiers and random forest fusion method under different sensor combinations are compared to verify that the proposed method can significantly improve fault diagnosis performance and provide new ideas for promoting the application of HVCB fault location. © 2020, Electrical Technology Press Co. Ltd. All right reserved.
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页码：421 / 431
页数：10
相关论文
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