Feature Extraction for Bearing Prognostics Based on Continuous Hidden Markov Model

Jian She Kang; Xing Hui Zhang; Jin Song Zhao; Lei Xiao

doi:10.4028/www.scientific.net/AMM.541-542.1483

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 541-542Feature Extraction for Bearing Prognostics Based...

Feature Extraction for Bearing Prognostics Based on Continuous Hidden Markov Model

Abstract:

Many research papers implemented fault diagnosis and prognosis when there are many history data. However, for some capital and high reliability equipment, it is very difficult to acquire some run-to-failure data. In this case, the fault diagnosis and prognosis become very hard. In order to address this issue, continuous hidden Markov model (CHMM) is used to track the degradation process in this paper. With the degradation, the log-likelihood which is the output of CHMM will decrease gradually. Therefore, this indicator can be used to evaluate the health condition of monitored equipment. Finally, bearing run-to-failure data sets are used to validate the model’s effectiveness

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Periodical:

Applied Mechanics and Materials (Volumes 541-542)

Pages:

1483-1486

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.541-542.1483

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Online since:

March 2014

Authors:

Jian She Kang, Xing Hui Zhang, Jin Song Zhao, Lei Xiao

Keywords:

Feature Extraction, Health Assessment, Hidden Markov Model (HMM), Prognostics

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