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Online Fault Feature Extraction and Abnormality Detection of ElectroHydraulic Servo Valve’s Condition
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HAO Shengqiao,XU Liming,SHEN Wei,WANG Jianlou
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(School of Mechanical Engineering, Shanghai Jiaotong University, Shanghai 200240, China)
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Abstract An online methodology of abnormality detection of electrohydraulic servo valve’s working condition was studied. A new feature extraction method based on the valve’s dynamic characteristics was proposed. One class support vector machine (SVM) was adopted to identify the working condition of the valve and the SVM parameters were optimized by crossvalidity estimation learning means. The results indicate that the proposed feature extraction method can effectively extract the fault features of the valve and its abnormal condition can be more effectively identified by taking advantage of oneclass support vector machine’s generalization ability than by the neural network, which lays the foundation for online faults diagnosis of electrohydraulic servo valve.
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Received: 16 October 2009
Published: 31 December 2010
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