Diagnosis of mechanical fault signals using continuous hidden Markov model

Jong Min Lee, Seung-Jong Kim, Yoha Hwang, Chang Seop Song

Research output: Contribution to journalArticle

86 Citations (Scopus)

Abstract

Hidden Markov Model (HMM) has been actively studied in speech recognition since 1960s and increasingly used in many other fields. However, its application to mechanical engineering has been very limited. HMM is not only very accurate and robust in analyzing signals but also can be a very powerful method of predicting target system's condition change. In this paper, continuous HMM (CHMM) has been tuned to be used in mechanical signal analysis and applied to diagnose of various mechanical signals including rotor fault signals. The results show HMM's big potential as an intelligent condition monitoring tool based on its accuracy, robustness, and forecasting ability.

Original languageEnglish
Pages (from-to)1065-1080
Number of pages16
JournalJournal of Sound and Vibration
Volume276
Issue number3-5
DOIs
Publication statusPublished - 2004 Sep 22
Externally publishedYes

Fingerprint

Hidden Markov models
mechanical engineering
signal analysis
Signal analysis
speech recognition
Condition monitoring
Mechanical engineering
Speech recognition
forecasting
rotors
Rotors

ASJC Scopus subject areas

  • Condensed Matter Physics
  • Acoustics and Ultrasonics
  • Mechanics of Materials
  • Mechanical Engineering

Cite this

Diagnosis of mechanical fault signals using continuous hidden Markov model. / Lee, Jong Min; Kim, Seung-Jong; Hwang, Yoha; Song, Chang Seop.

In: Journal of Sound and Vibration, Vol. 276, No. 3-5, 22.09.2004, p. 1065-1080.

Research output: Contribution to journalArticle

Lee, Jong Min ; Kim, Seung-Jong ; Hwang, Yoha ; Song, Chang Seop. / Diagnosis of mechanical fault signals using continuous hidden Markov model. In: Journal of Sound and Vibration. 2004 ; Vol. 276, No. 3-5. pp. 1065-1080.
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