NDLI: Dynamic Bayesian networks for machine diagnostics: hierarchical hidden Markov models vs. competitive learning

Content Provider	IEEE Xplore Digital Library
Author	Camci, F. Chinnam, R.B.
Copyright Year	2005
Description	Author affiliation: Dept. of Ind. & Manuf. Eng., Wayne State Univ., Detroit, MI, USA (Camci, F.; Chinnam, R.B.)
Abstract	The failure mechanisms of mechanical systems usually involve several degraded health states. Tracking the health state of a machine, even if the machine is working properly, is very critical for detecting, identifying, and localizing the failure (i.e., diagnosis) and estimating the remaining-useful-life of the component/machine (i.e., prognosis) for carrying out proper maintenance. Hidden Markov models (HMM) present us an opportunity to estimate these unobservable health states using observable sensor signals. Hierarchical HMM is composed of sub-HMMs in a hierarchical fashion, providing functionality beyond a HMM for modeling complex systems. Implementation of HMM based models as dynamic Bayesian networks (DBN) facilitates compact representation as well as additional flexibility with regard to model structure. Regular and hierarchical HMMs are employed here to estimate on-line the health state of drill-bits as they deteriorate with use on a CNC drilling machine. In the case of regular HMMs, each HMM (that is part of a committee) competes to represent a distinct health state and learns through competitive learning. In the case of hierarchical HMMs, health states are represented as distinct nodes in the top of the hierarchy. Detailed results from regular and hierarchical HMMs are very promising and are reported in this paper.
Starting Page	1752
Ending Page	1757
File Size	3798891
Page Count	6
File Format	PDF
ISBN	0780390482
DOI	10.1109/IJCNN.2005.1556145
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-07-31
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Bayesian methods Hidden Markov models Machine learning Automatic speech recognition State estimation Stochastic processes Failure analysis Mechanical systems Degradation Change detection algorithms
Content Type	Text
Resource Type	Article

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