Acoustic Emission Fault Diagnosis of Rolling Bearing Based on Discrete Hidden Markov Model

Acoustice emission (AE) technology has emerged as a promising diagnostic approach for rolling bearing fault detection. In this paper, the discrete hidden Markov chain model (DHMM) is used to diagnose faults based on AE signals. A tool built by MATLAB software is used to collect the acoustic emission signals of the rolling bearings for data reading and frame processing and then extract the vector that reflects the characteristics of the rolling bearing. The feature vectors are analyzed and diagnosed by using the DHMM. The results show that the DHMM method can provide reliable fault diagnosis for a rolling bearing.