YANG Ying-Chao, WANG Ti-Gang, DENG Wei-Jiang, LI Ren-Bing
Analogous circuit fault diagnosis is influenced by parameter selection of classical machine learning approach, the result of classification relies on parameter whether suitable or not, that is unable to carry on diagnosis online. This paper proposes an analogous circuit fault diagnosis model based on Relevant Vector Machine(RVM) from the sparse Bayesian theory, and improves the weight renewal algorithm. The hypothesis threshold value picks out unrelated weights before they approach infinity, this can reduce the algorithm running time and speed up the weight refresh. RVM can infer the discriminant function under the Bayesian framework. Moreover, it can obtain posterior probability of each classification, thus can judge the degree of classification result confidence, assist diagnosis decision-making. The result indicates that RVM need less relevance vectors than support vector machine with comparative default accuracy, sparser and generalizing. It suits to online fault detection.