Application of support vector machines in reciprocating compressor valve fault diagnosis

Authors:
Quanmin Ren;Xiaojiang Ma;Gang Miao
Affiliations:
Key Laboratory for Precision and Non-traditional Machining Technology of Ministry of Education, Dalian University of Technology, Dalian, P.R. China;Key Laboratory for Precision and Non-traditional Machining Technology of Ministry of Education, Dalian University of Technology, Dalian, P.R. China;Key Laboratory for Precision and Non-traditional Machining Technology of Ministry of Education, Dalian University of Technology, Dalian, P.R. China
Venue:
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
Year:
2005

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Abstract

Support Vector Machine (SVM) is a very effective method for pattern recognition. In this article, a intelligent diagnosis system based on SVMs is presented to solve the problem that there is not effective method for reciprocating compressor valve fault detection. The Local Wave method was used to decompose vibration signals, which acquired from valves surface, into sub-band signals. Then the higher-order statistics were calculated as the input features of classification system. The experiment results confirm that the classification technique has high flexibility and reliability on valve condition monitoring.