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Vol. 175, Issue 7, July 2014, pp. 207-213




Research on Bearing Fault Diagnosis Using APSO-SVM Method

1 Guangchun Yang, 2 Qingping Jian, 1 Hong Zhou

1 School of Mechanical Engineering of Panzhihua University, Panzhihua, 617000, China
2 School of Mechatronic Engineering, Southwest Petroleum University, Chengdu 610500, China
1 Tel: (+86) 13088367026, fax: (+86) 0812-3371017

1 E-mail: 1297208354@qq.com


Received: 7 June 2014 /Accepted: 7 July 2014 /Published: 31 July 2014

Digital Sensors and Sensor Sysstems


Abstract: According to the statistics, over 30 % of rotating equipment faults occurred in bearings. Therefore, the fault diagnosis of bearing has a great significance. To achieve effective bearing faults diagnosis, a diagnosis model based on support vector machine (SVM) and accelerated particle swarm optimization (APSO) for bearing fault diagnosis is proposed. Firstly, empirical mode decomposition (EMD) is adopted to decompose the fault signal into sum of several intrinsic mode function (IMF). Then, the feature vectors for bearing fault diagnosis are obtained from the IMF energy. Finally, the fault mode is identified by SVM model which is optimized by APSO. The experiment results show that the proposed diagnosis method can identify the bearing fault type effectively.


Keywords: Accelerated particle swarm optimization, Support vector machine, Parameter optimization, Fault diagnosis, EMD decomposition.


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