基于多特征融合与改进QPSO-RVM的万能式断路器故障振声诊断方法

doi:10.19595/j.cnki.1000-6753.tces.160769

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Abstract In order to diagnose the mechanical fault of conventional circuit breaker reliably, a vibration and acoustic joint diagnosis method on the switching fault of conventional circuit breakers based on multi-feature fusion and improved quantum particle swarm optimization (QPSO)-relevance vector machine (RVM) was proposed. Firstly, the vibration signal and acoustic signal were denoised by hard and soft threshold wavelet packet denoising algorithm and the processed signals were decomposed by complementary ensemble empirical mode decomposition (CEEMD). Then the energy coefficient, sample entropy and spectrum entropy were extracted from the intrinsic mode functions to form multi feature parameters. Secondly, it reduced the dimensionality of multi feature parameters by combined kernel function kernel principal component analysis and fused the result to form the feature vector as the input of RVM, solving the problem of low recognition accuracy and low stability of single feature. Finally, the improved QPSO was used to optimize classification model parameters, and the binary tree model based on RVM was established to identify machinery fault. Experiment results show that the proposed method can effectively improve the reliability of the diagnostic results at different machinery fault condition.

Key words： Conventional circuit breaker fault diagnosis vibration and acoustic feature fusion complementary ensemble empirical mode denoising(CEEMD) quantum particle swarm optimization (QPSO) relevance vector machine(RVM)

Received: 29 May 2016 Published: 25 October 2017

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TM561

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	Articles by authors
	Sun Shuguang
	Yu Han
	Du Taihang
	Wang Jingqin
	Zhao Liyuan

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Sun Shuguang,Yu Han,Du Taihang等. Vibration and Acoustic Joint Fault Diagnosis of Conventional Circuit Breaker Based on Multi-Feature Fusion and Improved QPSO-RVM[J]. Transactions of China Electrotechnical Society, 2017, 32(19): 107-117.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.160769 OR https://dgjsxb.ces-transaction.com/EN/Y2017/V32/I19/107

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