Volume 7 Number 9 (Sep. 2012)
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JCP 2012 Vol.7(9): 2324-2329 ISSN: 1796-203X
doi: 10.4304/jcp.7.9.2324-2329

Hybrid SVM-HMM Diagnosis Method for Rotor- Gear-Bearing Transmission System

Qiang Shao, Changjian Feng
Department of Mechanical Engineering, University of Dalian Nationalities Dalian Liaoning Province, China
Abstract—No stationary time series are occurring when the plant proceeds to an abnormal state or a transient situation from a normal state. So it is necessary to identify the type of fault during its early stages for the selection of appropriate operator actions to prevent a more severe situation. This paper proposes a new architecture for identification of the time series. It converts the output of support vector machine (SVM) into the form of posterior probability which is computed by the combined use of sigmoid function and Gauss model, it acts as a probability evaluator in the hidden states of hidden Markov models (HMM). Experiments show that the architecture is very effective.

Index Terms—HMM, SVM, identification, pattern recognition.

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Cite: Qiang Shao, Changjian Feng, "Hybrid SVM-HMM Diagnosis Method for Rotor- Gear-Bearing Transmission System," Journal of Computers vol. 7, no. 9, pp. 2324-2329, 2012.

General Information

ISSN: 1796-203X
Abbreviated Title: J.Comput.
Frequency: Monthly
Editor-in-Chief: Prof. Liansheng Tan
Executive Editor: Ms. Nina Lee
Abstracting/ Indexing: DBLP, EBSCO,  ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat, CNKI,etc
E-mail: jcp@iap.org
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