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《Journal of Central South University of Technology(Natural Science)》 2005-01
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Support Vector Machine and Its Application to Machinery Fault Diagnosis

HE Xue-wen~(1,2), ZHAO Hai-ming~1 (1.School of Mechanical and Electrical Engineering, Central South University, Changsha 410083, China; 2.School of Mechanical and Electrical Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China)  
Aiming at the difficulty in getting adequate fault samples, extracting eigenvectors and acquiring diagnosis knowledge in machinery fault diagnosis, a novel method for machinery fault diagnosis based on support vector machine(SVM) was put forward, in which wavelet packet analysis and signal energy decomposition were used for the feature extraction machinery faults. According to the method, the energy of different frequency bands after wavelet packet decomposition constitutes the input vectors of support vector machine as eigenvectors, and these eigenvectors were input into multiple fault classifiers to identify faults. The new method, by which multiple faults can be diagnosed, only requires a small quantity of fault samples and it doesn’t need the field knowledge of fault diagnosis. The experimental results show that different SVM classifiers, in which different kernel functions and their parameters are adopted, will influence the precision of fault classifiers. Under the circumstances that samples don’t include noise signal and samples include 15% noise signal, the classification precision of SVM classifiers is higher than that of BP artificial neural networks. Thus SVM classifiers show better classification performance.
【CateGory Index】: TP277
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