Gear Fault Diagnosis Method Based on Cyclostationary Degree and HMM

Abstract

This paper proposes a gear fault diagnosis method based on cyclostationary degree and Hidden Markov Model (HMM) theory. By using the demodulation characteristic of cyclostationary degree for AM and FM signals, we extract the characteristic information of the gear working status. This information can be converted into sets of fault feature vector, which is used as the training sample or observed sample of HMM model for gear fault identification. Experimental results show that the gear fault diagnosis method has good recognition result for the four kinds of status of normal, broken teeth, pitting and wear gear.