基于粗糙集神经网络和振动信号的高压断路器机械故障诊断

doi:10.19595/j.cnki.1000-6753.tces.L80285

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Abstract In order to accurately detect the type of mechanical failure of high-voltage circuit breakers, A fault diagnosis method for high-voltage circuit breaker vibration signals based on the combination of the intrinsic mode function (IMF) spectrum energy and the rough set neural network is proposes. Firstly, the circuit breaker vibration signal is decomposed by empirical mode decomposition (EMD), and then several IMF is obtained. Hilbert transform is performed on each IMF component to obtain the Hilbert marginal spectrum. Its square is called the marginal spectrum energy as the feature vector. Based on the rough set theory, the attribute reduction analysis is performed on the eigenvectors to establish a simple and clear decision table. The radial basis function (RBF) neural network fault model is established according to the decision table rules. Experimental results show that this method can effectively classify the types of mechanical faults of high-voltage circuit breakers.

Key words： High-voltage circuit breaker intrinsic mode marginal spectrum energy rough set neural network vibration signals fault diagnosis

Received: 29 June 2018 Published: 05 March 2020

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TM561

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Cite this article:

Lin Lin,Chen ZhiYing. Mechanical Fault Diagnosis of High Voltage Circuit Breakers Based on Rough Set Neural Networks and Vibration Signals[J]. Transactions of China Electrotechnical Society, 2020, 35(zk1): 277-283.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.L80285 OR https://dgjsxb.ces-transaction.com/EN/Y2020/V35/Izk1/277

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