Abstract: In order to correctly recognize the current degradation state of equipment for preventing equipment from farther degradating and going wrong, a new method of equipment degradation state recognition based on wavelet correlation feature scale entropy(WCFSE) and HSMM is proposed. The gathered vibration signal of equipment is processed by way of the wavelet transform correlation filter(WTCF). In order to get the high signal-to-noise scales wavelet coefficients, the conception of WCFSE is presented based on the integration of information entropy theory and WTCF, and the WCFSE eigenvectors of signal are constructed. Those WCFSE eigenvectors are inputted to the HSMM for training, and a running state classifier of medical equipment based on HSMM is constructed to recognize the equipment degradation state. Roller bearings are taken as examples, and the running states of a normal rolling element and several rolling elements with different degree of fault are recognized by using the proposed method. This method is compared with the state recognition method based on WCFSE and HMM, experimental results show that this method can effectively recognize the degradation state of equipment.

Key words: Hidden semi-Markov models (HSMM), Degradation state, State recognition, Wavelet correlation feature scale entropy