Simultaneous Fault Diagnosis of Main Retarder Using Improved Paired Relevance Vector Machine Based on Multi-Kernel Learning

Abstract

According to the practical requirement of auto manufacturer, excellent fault diagnosis system aiming at simultaneous fault is indispensable for main retarder of automobile. This paper proposes a novel diagnosis method which employs wavelet package transform and sample entropy to achieve feature extraction, later utilize relevance vector machine to construct a set of paired classifiers. Considering that features extracted from vibration signal are multiple and heterogeneous, we combine multi-kernel learning and relevance vector machine together and optimize kernel function parameters by using incremental learning, cross validation and genetic algorithm. Comparing with SVM and PNN, the experiment results verify high diagnosis accuracy and low computational cost of the proposed method.