A heuristic optimization algorithm for HMM based on SA and EM in machinery diagnosis
This paper proposes a novel hidden Markov model (HMM) based on simulated annealing (SA) algorithm and expectation maximization (EM) algorithm for machinery diagnosis. As traditional HMM is sensitive to initial values and EM is easy to trap into a local optimization, SA is combined to improve HMM which can overcome local optimization searching problem. The proposed HMM has strong ability of global convergence, and optimizes the process of parameters estimation. Finally, through a case study, the computation results illustrate this SAEM-HMM has high efficiency and accuracy, which could help machinery diagnosis in practical.
KeywordsHidden Markov model Expectation maximization Simulated annealing Diagnosis
The authors would like to thank anonymous referees for their remarkable comments and great support by National Natural Science Foundation of China (71301176) and Doctoral Program of Higher Education of China (No. 20130191120001).
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