Journal of Mechanical Science and Technology

The Korean Society of Mechanical Engineers (대한기계학회)
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Fault Diagnosis of Rotating Machinery Based on Multi-Class Support Vector Machines

  • Yang Bo-Suk (School of Mechanical Engineering, Pukyong National University) ;
  • Han Tian (School of Mechanical Engineering, Pukyong National University) ;
  • Hwang Won-Woo (School of Mechanical Engineering, Pukyong National University)
  • Published : 2005.03.01
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Abstract

Support vector machines (SVMs) have become one of the most popular approaches to learning from examples and have many potential applications in science and engineering. However, their applications in fault diagnosis of rotating machinery are rather limited. Most of the published papers focus on some special fault diagnoses. This study covers the overall diagnosis procedures on most of the faults experienced in rotating machinery and examines the performance of different SVMs strategies. The excellent characteristics of SVMs are demonstrated by comparing the results obtained by artificial neural networks (ANNs) using vibration signals of a fault simulator.

Keywords

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