Health-state estimation and prognostics in machining processes

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dc.contributor.author Camci, Fatih -
dc.contributor.author Chinnam, R. B. -
dc.date.accessioned 2012-01-23T23:05:18Z
dc.date.available 2012-01-23T23:05:18Z
dc.date.issued 2010-07-02T00:00:00Z -
dc.identifier.citation F. Camci, R.B. Chinnam, Health-state estimation and prognostics in machining processes, IEEE Transactions on Automation Science and Engineering, Volume 7, Issue 3, 2010, Pages 581 - 597.
dc.identifier.issn 1545-5955 -
dc.identifier.uri http://dx.doi.org/10.1109/TASE.2009.2038170 -
dc.identifier.uri http://dspace.lib.cranfield.ac.uk/handle/1826/6877
dc.description.abstract Failure mechanisms of electromechanical systems usually involve several degraded health-states. Tracking and forecasting the evolution of health-states and impending failures, in the form of remaining-useful-life (RUL), is a critical challenge and regarded as the Achilles' heel of condition-based-maintenance (CBM). This paper demonstrates how this difficult problem can be addressed through Hidden Markov models (HMMs) that are able to estimate unobservable health-states using observable sensor signals. In particular, implementation of HMM based models as dynamic Bayesian networks (DBNs) facilitates compact representation as well as additional flexibility with regard to model structure. Both regular HMM pools and hierarchical HMMs are employed here to estimate online 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 pyramid structure, providing functionality beyond an HMM for modeling complex systems. In the case of regular HMMs, each HMM within the pool competes to represent a distinct health-state and adapts through competitive learning. In the case of hierarchical HMMs, health-states are 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. Detailed results on health-state and RUL estimation are very promising and are reported in this paper. Hierarchical HMMs seem to be particularly effective and efficient and outperform other HMM methods from literature. en_UK
dc.language.iso en_UK -
dc.publisher IEEE en_UK
dc.rights (c) 2010 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
dc.subject Condition-based-maintenance , diagnostics , dynamic Bayesian networks , health- state estimation , hidden Markov models , prognostics , remaining-useful-life en_UK
dc.title Health-state estimation and prognostics in machining processes en_UK
dc.type Article -


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