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Journal of Intelligent Manufacturing
Erschienen in: Journal of Intelligent Manufacturing 8/2018

22.04.2016

A heuristic optimization algorithm for HMM based on SA and EM in machinery diagnosis

verfasst von: Wenzhu Liao, Dan Li, Shihao Cui

Erschienen in: Journal of Intelligent Manufacturing | Ausgabe 8/2018

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Abstract

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.
Vorheriger Artikel A new method of reusing the manufacturing information for the slightly changed 3D CAD model
Nächster Artikel A novel inverted fuzzy decoupling scheme for MIMO systems with disturbance: a case study of binary distillation column

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Metadaten
Titel
A heuristic optimization algorithm for HMM based on SA and EM in machinery diagnosis
verfasst von
Wenzhu Liao
Dan Li
Shihao Cui
Publikationsdatum
22.04.2016
Verlag
Springer US
Erschienen in
Journal of Intelligent Manufacturing / Ausgabe 8/2018
Print ISSN: 0956-5515
Elektronische ISSN: 1572-8145
DOI
https://doi.org/10.1007/s10845-016-1222-1

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