FAULT DIAGNOSIS TECHNIQUE  OF ROTATING

 MACHINE BASED ON CHMM

SONG Xueping  MA Hui  MAO Guohao  WEN Bangchun

(School of Mechanical Engineering & Automation,Northeastern University, Shenyang 110004)

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

CLC No: O322  TH165.3

国家自然科学基金资助项目(50275024). Received 20050530, received in revised form 20051226