Research on Fault Diagnosis Method Using Improved Multi-Class Classification Algorithm and Relevance Vector Machine

Kun Wu, Jianshe Kang and Kuo Chi
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Kun Wu: Department of Equipment Management, Mechanical Engineering College, Hebei, China
Jianshe Kang: Department of Equipment Management, Mechanical Engineering College, Hebei, China
Kuo Chi: Department of Equipment Management, Mechanical Engineering College, Hebei, China

International Journal of Information Technology and Web Engineering (IJITWE), 2015, vol. 10, issue 3, 1-16

Abstract: In view of the problems in traditional fault diagnosis method, such as small samples and nonlinear relations, a fault diagnosis method based on improved multi-class classification algorithm and relevance vector machine (RVM) is proposed in the paper. Through improving the majority-vote strategy of traditional One-Against-One (OAO) algorithm and combining the features of OAO and One-Against-Rest (OAR) algorithms, the k-class classification problem is transformed into k(k-1)/2 three-class classification problems based on the proposed majority-vote strategy of double-layer and thereby an improved multi-class classification algorithm of One-Against-One-Against-Rest (OAOAR) is presented. And on each three-class classification issue, OAO and RVM as the binary classifier are adopted to achieve the multi-class classification of RVM. Numerical simulations of UCI datasets and fault diagnostic experiments results of power transformers both demonstrate that the proposed method performs significantly better than other traditional methods in terms of increasing the diagnostic accuracy, optimizing the voting results, strengthening the diagnostic confidence and identifying the hidden classes, and has more practical value in engineering.

Date: 2015
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