NDLI: Hierarchical HMMs for Autonomous Diagnostics and Prognostics

Content Provider	IEEE Xplore Digital Library
Author	Camci, F. Chinnam, R.B.
Copyright Year	2006
Description	Author affiliation: Impact Technol., Rochester (Camci, F.)
Abstract	The ability to effectively control maintenance and support costs under ever increasing global competition not only requires timely identification of impending failures of critical equipment but also calls for accurate estimation of remaining-useful-life (RUL). This is especially important for critical systems with high maintenance and logistics cost such as military systems and nuclear plants. Accurately forecasting the failure time (i.e., prognostics) can lead to failure prevention and diminish the time and cost associated with unnecessary maintenance. This paper presents a prognostic method that relies on hidden Markov models (HMMs) for health-state forecasting. HMMs present an opportunity to estimate unobservable health-states using observable sensor signals. In particular, implementation of HMM based models as dynamic Bayesian networks (DBNs) improves the design and realization of flexible HMM structures (such as auto-regressive HMMs and hierarchical HMMs) for representing complex systems. 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. Hierarchical HMM is composed of sub-HMMs in a hierarchical fashion such that health-states can be represented as distinct nodes at the top of the hierarchy. Monte Carlo simulation, with state transition probabilities derived from a hierarchical HMM, is employed for RUL estimation. Results on health-state and RUL estimation are very promising and are reported in this paper.
Starting Page	2445
Ending Page	2452
File Size	9907817
Page Count	8
File Format	PDF
ISBN	0780394909
DOI	10.1109/IJCNN.2006.247092
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-07-16
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hidden Markov models Costs Logistics Bayesian methods Degradation Humans Computer numerical control Drilling machines State estimation Condition monitoring hidden Markov models Condition-based-maintenance diagnostics health-state estimation prognostics remaining-useful-life dynamic Bayesian networks
Content Type	Text
Resource Type	Article

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