An intelligent fault diagnosis approach of analog circuit based on KLDA and OAOSVM

Fault diagnosis of analog circuits is essential for guaranteeing the reliability and maintainability of electronic systems. Analog circuit fault diagnosis can be regarded as the pattern recognition issue and addressed by machine learning theory. In this paper, a novel analog circuit fault diagnosis approach based on kernel linear discriminant analysis (KLDA) and multi-class support vector machine is proposed. In order to obtain a successful SVM-based fault classifier, dimensionality reduction and fault features extraction are very important. Due to better performance of nonlinear fault features extraction as compared with LDA, KLDA is adopted in the proposed approach. The extracted fault features are then used as the inputs of one-against-one SVMs to solve fault diagnosis issue. The effectiveness of the proposed approach is demonstrated by the experimental results.