Degradation assessment for machinery prognostics using Hidden Markov Models

Hai Qiu, Haitao Liao, Jay Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

14 Scopus citations

Abstract

Degradation detection and recognition of degradation pattern are crucial to the successful deployment of prognostics. A machine degradation process is known to be stochastic instead of deterministic. Recognizing the degradation pattern needs helps from stochastic and probabilistic models. Among various stochastic approaches, Hidden Markov Models (HMMs) have been proven to be very effective in modeling both dynamic and static signals [1]. In this paper, aiming to providing a guideline of how to effectively and efficiently use the HMMs to assess degradation for various machinery prognostic applications, three different approaches of applying the HMMs are reviewed and compared. It demonstrates that depending on the varieties of applications, available prior knowledge, and characteristics of degradation processes, those three implementation approaches perform differently. A full understanding of the strengths and weaknesses of each deployment approach is extremely important in order to effectively utilize this powerful tool for system degradation assessment.

Original languageEnglish (US)
Title of host publicationProc. of the ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conferences - DETC2005
Subtitle of host publication20th Biennial Conf. on Mechanical Vibration and Noise
Pages531-537
Number of pages7
StatePublished - Dec 1 2005
EventDETC2005: ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference - Long Beach, CA, United States
Duration: Sep 24 2005Sep 28 2005

Publication series

NameProceedings of the ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference - DETC2005
Volume1 A

Other

OtherDETC2005: ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
CountryUnited States
CityLong Beach, CA
Period9/24/059/28/05

ASJC Scopus subject areas

  • Engineering(all)

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    Qiu, H., Liao, H., & Lee, J. (2005). Degradation assessment for machinery prognostics using Hidden Markov Models. In Proc. of the ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conferences - DETC2005: 20th Biennial Conf. on Mechanical Vibration and Noise (pp. 531-537). (Proceedings of the ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference - DETC2005; Vol. 1 A).