A Method of Mechanical Fault Identification of Permanent Magnet Repulsion Mechanism of Vacuum Circuit Breaker Based on Chaos Attractor

被引：0
|
作者：
Liu X. [1 ]
Zhang X. [1 ]
Jiang W. [2 ]
Chen H. [1 ]
Chen J. [3 ]
Song B. [1 ]
机构：
[1] State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin
[2] School of Mechanical Engineering Tiangong University, Tianjin
[3] Chengdu Xuguang Electronics Co. Ltd Chengdu
来源：
Diangong Jishu Xuebao/Transactions of China Electrotechnical Society | 2022年 / 37卷 / 20期
关键词：
attractor morphology; oil buffer; repulsion mechanism; support vector machine; Vacuum circuit breaker;
D O I：
10.19595/j.cnki.1000-6753.tces.210970
中图分类号：
学科分类号：
摘要：
In order to solve the problem that the vibration signal of repulsive mechanism of vacuum circuit breaker is transient, nonlinear, and it is difficult to quantitatively evaluate and identify the fault, this paper proposes a method of repulsive mechanism fault evaluation and identification based on chaotic attractor moment feature of attractor morphology and particle swarm optimization support vector machine. The typical faults of the repulsive mechanism in the process of opening and closing, especially the vibration signals of the opening oil buffer with different degrees of overmodulation fault, are analyzed. Firstly, the phase space reconstruction method of chaos theory and Wolf algorithm are used to obtain the maximum Lyapunov exponent, which shows that the vibration signal of repulsive mechanism has obvious chaotic characteristics. The evolution of chaotic attractor of the vibration signal is qualitatively analyzed using three-dimensional phase diagram. Then, the chaotic attractor moment theory is introduced to extract four kinds of two-dimensional attractor moments. The slope of the first region curve is calculated as the attractor morphological feature by the least square linear regression method, and the time-domain signals are extracted to form the feature vector library. Finally, the accuracy of the support vector machine algorithm and the particle swarm optimization-support vector machine combination algorithm is compared. The experimental results show that the proposed method can accurately identify the fault types of repulsive mechanism. © 2022 Chinese Machine Press. All rights reserved.
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页码：5334 / 5346
页数：12
相关论文
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