Use of a (MSPCA) and (SVM) Method for Diagnosis of Motor Faults

Wen Ying Chen; Ya Nan Wang; Xue Fei Wu; Yu Xiang Qu

doi:10.4028/www.scientific.net/AMM.321-324.114

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Use of a (MSPCA) and (SVM) Method for Diagnosis of Motor Faults
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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 321-324Use of a (MSPCA) and (SVM) Method for Diagnosis of...

Use of a (MSPCA) and (SVM) Method for Diagnosis of Motor Faults

Abstract:

This paper uses the combination between support vector machine and multi-scale principal component analysis. For motor fault detection, the principal component model can be established in various scales. Through T2 and Q statistic judgment whether motor can run normally. The experimental results show that the method of combination vector machine and multi-scale principal component analysis is supported to diagnose motor fault. This offers a new method and idea to diagnose motor. This method improves the accuracy of motor fault detection and practical significance.

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Periodical:

Applied Mechanics and Materials (Volumes 321-324)

Pages:

114-117

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.321-324.114

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Online since:

June 2013

Authors:

Wen Ying Chen, Ya Nan Wang, Xue Fei Wu, Yu Xiang Qu

Keywords:

Fault Diagnosis, Multi-Scale, Principal Component Analysis (PCA), Upport Vector Machine

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