基于SOM的真空断路器机械故障诊断

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Abstract The basic concepts and highlights of self-organization map (SOM) neural network which is suitable for small samples are given. The relationship between mechanical characteristics of vacuum circuit breaker and the corresponding mechanical failure was analyzed. On this basis, a method of mechanical fault diagnosis of vacuum circuit breaker that mechanical characteristics parameters were taken as training and identification samples of SOM neural network is proposed. The whole process of applying this method to conduct the mechanical fault classification of circuit breakers is emphasized: the fault differentiation is carried out by extracting the mechanical characteristics of circuit breaker under the normal and fault states as inputs of SOM network. The experimental results show that the fault diagnosis scheme achieves high accuracy of mechanical fault classification for vacuum circuit breakers.

Key words： Vacuum circuit breaker self-organization map mechanical characteristics fault diagnosis

Received: 20 September 2015 Published: 22 March 2017

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TM561

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	Liu Yan
	Chen Li’an

Cite this article:

Liu Yan,Chen Li’an. Mechanical Fault Diagnosis of Vacuum Circuit Breaker Based on SOM[J]. Transactions of China Electrotechnical Society, 2017, 32(5): 49-54.

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https://dgjsxb.ces-transaction.com/EN/ OR https://dgjsxb.ces-transaction.com/EN/Y2017/V32/I5/49

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