关键词: 故障诊断, 多核学习, 散度矩阵, 超限学习机, 自适应正则化

Abstract: In order to improve the fault diagnosis accuracy of analog circuit, a novel multi-kernel Extreme Learning Machine （ELM） diagnostic model is presented by combining with feature selection algorithm used one-dimensional ambiguity among fault features. In this model, the optimization of regularization factor is incorporated into the solving process of basis kernel weight coefficients by setting a fictitious kernel function. Moreover, the within-class scatter of training data in feature space is also incorporated into optimized objective function of multi-kernel ELM, which makes the samples from same fault pattern more concentrated when the training error is minimized so that the identifiability is effectively enhanced. Experimental results on two analog circuits show that the diagnostic accuracy is significantly improved compared with single kernel learning algorithms, and those faults which are difficult to be identified can be more accurately isolated into relevant ambiguity groups. In addition, compared with common multi-kernel learning algorithms, the similar diagnostic results can be obtained, but the proposed model costs less time.

Key words: fault diagnosis, multiple kernel learning, scatter matrix, extreme learning machine, adaptive regularization