Abstract: Hidden Markov model(HMM) as a tool for disposing signal pattern which has great ability of building time sequence, has widely been used in speech recognition. It is especially fit for signal which is nonlinear, non-stationary, bad in repeating to analysis. Based on the comparability between vibration signal and sound signal, CHMM is introduced to fault diagnosis for rotating machine. CHMM is built by using 12 rank LPC cepstrum coefficient to extract feature vectors, scaled forwards-backwards algorithm is introduced to calculate log-likelihood avoiding the data to underflow and K-means algorithm is also used to initialize the parameter. In the given observation sequence, optimizing every model with Viterbi algorithm, with baum-welch algorithm to re-estimate parameter, and the re-estimation formula is also provided. Last, four kinds of fault experiment have been simulated on the rotor test-bed, and four kinds of fault CHMM model are built. Machine’s operating state is determined by calculating the maximal log-likelihood, and the results of experiment proves that this kind of method is effective.

Key words: CHMM, Faults diagnosis, Rotating machine Pattern recognition