Blind identification of threshold auto-regressive model for machine fault diagnosis

Abstract

A blind identification method was developed for the threshold auto-regressive (TAR) model. The method had good identification accuracy and rapid convergence, especially for higher order systems. The proposed method was then combined with the hidden Markov model (HMM) to determine the auto-regressive (AR) coefficients for each interval used for feature extraction, with the HMM as a classifier. The fault diagnoses during the speed-up and speed-down processes for rotating machinery have been successfully completed. The result of the experiment shows that the proposed method is practical and effective.