Hidden Markov Model And Its Application In Mechanical Faults Pattern Recognition

Hidden Markov Model (HMM) is a new technique in pattern recognition. By extracting and vector quantizing the features of training signal, Hidden Markov Mode with according state number and observation number can be established, then the similarity probability of the unknown signal can be calculated through HMM. By comparing the similarity probability, pattern of the signal can be recognized. In this work, HMM is applied in the pattern recognition and fault diagnosis of mechanical equipment, The mean works include three parts: 1) The realization approach of HMM for fault diagnosis was discussed and the program of HMM was developed with Matlab; 2) The methods of feature extraction from vibration signal, especially cepstrum and envelop method, was studied, and according program was developed with LabVIEW; 3) Two typical rotating machine(turbine and gearbox) with different signal characteristics were used to improve the effectiveness of HMM for identification and classification of machine condition. For the turbine, the amplitude at the basic frequency is extracted as the features for HMM training, for the gear box, HMM is established to recognize the development of gear crack. The results of these two examples are all satisfied.