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Proceedings of 2009 International Workshop on Information Security and Application (IWISA 2009)

Qingdao, China, November 21-22, 2009

Editors: Feng Gao and Xijun Zhu

AP Catalog Number: AP-PROC-CS-09CN004

ISBN: 978-952-5726-06-0

Page(s): 546-549

Fault Diagnosis System for Reciprocating Air Compressor Based on Support Vector Machine

††††††† Sheng Fu, Jing Li, and Yabin Zhang

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Reciprocating air compressorís structure is complex, and it has various excitation sources when running, moreover, there are a few fault samples in actual fault diagnosis, so it is difficult to implement intelligent diagnosis. Support Vector Machine based on Statistical Learning Theory just overcomes this deficiency, and it provides a new approach for diagnosis technology to develop into intelligent diagnosis. The application of Support Vector Machine on fault diagnosis for reciprocating air compressor and a concrete implementation scheme are discussed in this paper. A fault diagnosis system for reciprocating air compressor is established, and the vibration signals of rolling bear in reciprocating air compressorís crankcase are simulated in a test-bed. The test result shows that this system has strong adaptability for reciprocating air compressor diagnosis of a few samples and could recognize fault rapidly and accurately.

Index Terms

reciprocating air compressor, Support Vector Machine, fault diagnosis

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