Pattern recognition of mechanical fault signal using hidden markov model

Hidden Markov Model (HMM) is a very powerful signal analysis method. However, despite its huge success in voice recognition, its use in mechanical engineering has been very limited. In this paper, HMM algorithm has been optimized to be used in mechanical signal analysis and was successfully applied to recognition of rotor fault patterns. Both continuous (CHMM) and discrete (DHMM) types were studied. Rotor kit was set under unbalance and oil whirl conditions and time signals of two failure conditions were sampled and converted to auto-power spectrums. Using filter bank, feature vectors were extracted from auto-power spectrum. After CHMMs and DHMMs were trained, each HMM was applied to all sampled data and has shown accurate rotor fault recognition ability. HMM has shown good recognition ability in spite of small number of training data set. Judging by the result, CHMM has better recognition ability and DHMM has more robust recognition ability in this study.