Bearing Operating State Evaluation Based on Improved HMM

摘要

With the development of industry, the fault diagnosis requirements for rolling bearings are getting higher and higher. This paper aims to develop low-complexity solutions for bearing fault diagnosis. In this paper, we use wavelet decomposition to obtain gesture Monitoring Index Vector (MIVs), after this, an improved Hidden Markov Model (HMM) algorithm was proposed for bearing fault diagnosis, in which we apply the Genetic Algorithm (GA) to avoid the convergence to local optimum, thus improving the recognition performance. The experimental results on 11 groups of test datasets demonstrate that the proposed algorithm (GAHMM) can achieve a higher average recognition rate of 93%, 87%, 87%, 93%, 93%, 97%, 100%, 97%, 97%, 100%, 97%.