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|Document Type: ||Article|
|Title: ||Health-state estimation and prognostics in machining processes|
|Authors: ||Camci, Fatih|
Chinnam, R. B.
|Issue Date: ||2010|
|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.|
|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.|
|Appears in Collections:||Staff publications - School of Applied Sciences|
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