Online Degradation Assessment and Adaptive Fault Detection Using Modified Hidden Markov Model
SCIE
SCOPUS
- Title
- Online Degradation Assessment and Adaptive Fault Detection Using Modified Hidden Markov Model
- Authors
- LEE, SEUNG CHUL; Li, Lin; Ni, Jun
- Date Issued
- 2010-04
- Publisher
- ASME-AMER SOC MECHANICAL ENG
- 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.
- Keywords
- hidden Markov model; online degradation assessment; adaptive fault detection
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/41159
- DOI
- 10.1115/1.4001247
- ISSN
- 1087-1357
- Article Type
- Article
- Citation
- JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING-TRANSACTIONS OF THE ASME, vol. 132, no. 2, 2010-04
- Files in This Item:
- There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.