Fault feature extraction and recognition of vibration signal based on CWD-SK and HMM

Abstract

Aiming at the problem of rolling bearing fault diagnosis caused by interference information in the process of vibration transmission, this paper puts forward the theory and method of vibration signal fault diagnosis based on Choi Williams distribution spectral kurtosis (SK) combined with hidden Markov model (HMM). The fault location method of local mean decomposition (LMD) is used to screen the PF component with obvious fault characteristic information, CWD-SK is calculated and set as the characteristic parameter to realize the feature extraction of the initial fault of the vibration signal. CWD-SK is input into HMM as the feature vector to classify and identify the fault features of the vibration signal. Finally, the effectiveness of the algorithm is verified by the simulation of the actual data. It provides a robust theoretical and methodological basis for the establishment of fault diagnosis and classification recognition technology suitable for initial rolling mechanical vibration signals.