Fault Diagnosis of Aircraft Electromechanical System Based on SA-HMM

Typical hidden Markov model (HMM) is very sensitive to initial parameters while using random parameters to train HMM often falls into local optimum, and the effect is poor when applied to fault diagnosis. Fault diagnosis algorithm based on simulated annealing algorithm (SA) and hidden Markov model is proposed in this paper. Selecting the best simulated annealing algorithm parameters and combining with Baum-Welch algorithm, we optimize the hidden Markov model and apply it to the rolling bearing fault diagnosis. Experimental results show that the new algorithm for fault diagnosis accurate rate has increased significantly.