基于混沌吸引子的真空断路器永磁斥力机构机械故障识别方法

doi:10.19595/j.cnki.1000-6753.tces.210970

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Abstract 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.

Key words： Vacuum circuit breaker repulsion mechanism oil buffer attractor morphology support vector machine

Received: 02 July 2021

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TM561

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	Liu Xiaoming
	Zhang Xusong
	Jiang Wentao
	Chen Hai
	Chen Junping
	Song Boyang

Cite this article:

Liu Xiaoming,Zhang Xusong,Jiang Wentao等. A Method of Mechanical Fault Identification of Permanent Magnet Repulsion Mechanism of Vacuum Circuit Breaker Based on Chaos Attractor[J]. Transactions of China Electrotechnical Society, 2022, 37(20): 5334-5346.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.210970 OR https://dgjsxb.ces-transaction.com/EN/Y2022/V37/I20/5334

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