基于骨干微粒群算法和支持向量机的电机转子断条故障诊断

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Abstract In order to accurately recognize the broken rotor bar fault of induction motors, a novel method for fault diagnosis is proposed based on the bare-bones particle swarm optimization algorithm (BBPSO) and support vector machine(SVM), and feasible diagnostic steps and analysis are also introduced. Firstly, a fundamental-frequency filtering method based on BBPSO is proposed to eliminate the influence of fundamental wave in fault characteristic components. Then the feature vector of induction motor in different conditions is extracted with wavelet packet, by which the residual current is decomposed to series of frequency bands; and is considered as the input vector of SVM. The “one-against-one” vector machine is used to solve the multi-class classification problem, and the BBPSO and cross-validation are taken to optimize model parameters. Finally, the experiment shows that the proposed method is effective to diagnose the broken rotor bars fault of induction motors.

Key words： Induction motors broken rotor bars bare-bone particle swarm optimization wavelet packet support vector machine fault diagnosis

Received: 12 July 2012 Published: 16 June 2014

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TM343

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	Shi Liping
	Wang Panpan
	Hu Yongjun
	Han Li

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Shi Liping,Wang Panpan,Hu Yongjun等. Broken Rotor Bar Fault Diagnosis of Induction Motors Based on Bare-Bone Particle Swarm Optimization and Support Vector Machine[J]. Transactions of China Electrotechnical Society, 2014, 29(1): 147-155.

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http://dgjsxb.ces-transaction.com/EN/ OR http://dgjsxb.ces-transaction.com/EN/Y2014/V29/I1/147

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