Fault Diagnosis of Gear Based on Permutation Entropy and CHMM

Considering the difficulties of fault feature extraction and state recognition, a fault diagnosis method for gear based on permutation entropy and Continuous Hidden Markov Model (CHMM) has been proposed. After the vibration signals of gear were denoised, the noise-reducted signals were analyzed by permutation entropy method to extract the mean, root-mean-square, maximum and minimum values. Then CHMM was adopted to carry out gear diagnosis by training algorithm and comparing the maximum log-likelihood values. Finally the four gear states of normal, mild wear, heavy wear and broken were tested by gearbox simulation experiment. The results show that this method can diagnose the fault of gears effectively.