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Lee, Seungchul
iSystems Design Lab
Research Interests
  • Intelligent design for products and manufacturing systems

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Online Degradation Assessment and Adaptive Fault Detection Using Modified Hidden Markov Model

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Title
Online Degradation Assessment and Adaptive Fault Detection Using Modified Hidden Markov Model
Author
Lee, SeungchulLi, LinNi, Jun
Keywords
Adaptive fault detection; Hidden Markov model; Online degradation assessment
Issue Date
2010-04
Publisher
ASME-AMER SOC MECHANICAL ENG
Citation
JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING-TRANSACTIONS OF THE ASME, v.132, no.2, pp.1 - 11
Abstract
Online condition monitoring and diagnosis systems play an important role in the modern manufacturing industry. This paper presents a novel method to diagnose the degradation processes of multiple failure modes using a modified hidden Markov model (MHMM) with variable state space. The proposed MHMM is combined with statistical process control to quickly detect the occurrence of an unknown fault. This method allows the state space of a hidden Markov model to be adjusted and updated with the identification of new states. Hence, the online degradation assessment and adaptive fault diagnosis can be simultaneously obtained. Experimental results in a turning process illustrate that the tool wear state can be successfully detected, and previously unknown tool wear processes can be identified at the early stages using the MHMM.
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DOI
10.1115/1.4001247
ISSN
1087-1357
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DHE_Journal Papers
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