Incipient Fault Diagnosis of Analog Circuits Based on MHMM

MHMM is a Hidden Markov Model(HMM) model with mixture of Gaussians output,which has fine pattern recognition capability and is suitable for dealing with mixed samples.Analog circuit is complicated,the incipient faults are various,and the samples of incipient faults are mixed severely.Therefore,we put forward a MHMM based method for incipient fault diagnosis of analog circuits.First,the dimensions of the experimental samples were decreased with the Linear Discriminant Analysis(LDA) technology,and the observation sequences with the lower dimensions were obtained,and the samples were partitioned primarily.Then,the observation sequences were approached by Gaussian Mixture Model(GMM),and the MHMM model was built up by using them.The experimental results showed that:compared with BP neural network,MHMM method is more advantageous for incipient fault diagnosis.