Research on fault diagnosis for reciprocating compressor valve using information entropy and SVM method |
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| Authors: | Houxi Cui Laibin Zhang Rongyu Kang Xinyang Lan |
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| Institution: | 1. Collaborative Innovation Center for Industrial Internet of Things, College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;2. School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ 85287, USA;3. Research Center of China National Offshore Oil Corporation, Beijing 100010, China;4. College of Automation, Chongqing University, Chongqing 400044, China;5. College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China |
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| Abstract: | A method of compressor valve fault diagnosis using information entropy and SVM is proposed in this paper. The main obstacle in the fault diagnosis focuses on the low non-linear pattern recognition performance and small sample number. Therefore, the information entropy, which is flexible and tolerant to the non-linearity problem, is applied to analyze the characteristic of the signals. SVM is employed in the fault classification because of its superiority in dealing with smaller sample problem. The information entropy features and the optimization test of the SVM model are detailed analyzed. The experiment shows the good performance of the information entropy SVM method in compressor valve fault diagnosis. |
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