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

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

Kun Wu (Department of Equipment Management, Mechanical Engineering College, Hebei, China), Jianshe Kang (Department of Equipment Management, Mechanical Engineering College, Hebei, China) and Kuo Chi (Department of Equipment Management, Mechanical Engineering College, Hebei, China)
DOI: 10.4018/IJITWE.2015070101
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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.
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1. Introduction

With various kinds of intelligent classification methods studied, support vector machine has recently been widely applied in the fault diagnosis of many engineering fields due to the unique advantages when faced with small sample data of faults and highly nonlinear relations between monitoring data and the actual system status (Saravanan, N., 2010; Guo, C., 2010; Wu, Q., 2010; Cui, H., 2009; Salahshoor, K., 2010). However, the disadvantages of SVM are also obvious including that the number of kernel function will increase significantly with increasing samples, the configuration of kernel parameters and regularization coefficients is complex and the kernel function must meets the Mercer conditions.

Relevance vector machine (RVM) is a sparse probability model based on Bayesian learning theory which has a similar function form with SVM and better generalization performance (Tipping, M. E., 2000; Tipping, M. E., 2001). Furthermore, it does not have to satisfy the Mercer conditions and has higher model sparsity. And the model is capable of outputting probability distribution with prediction results, and widely applied to engineering fields such as sewage detection (Zeng, T., 2013), hyperspectral image classification (Demir, B., 2007; Zhao, C., 2012; Yang, G., 2010), fault diagnosis and prognostics (Huang, K., 2010; Moghanjooghi, H. A., 2012) and so on nowadays.

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