A Methodology for Updating Prognostic Models via Kalman Filters

Venkatesh Rajagopalan and Arun Subramaniyan
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Special Issue Big Data and Analytics
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Full Paper
ijphm_16_037.pdf1.79 MBFebruary 2, 2017 - 6:42pm

Prognostic models are built to predict the future evolution of the state or health of a system. Typical applications of these models include predictions of damage (like crack, wear) and estimation of remaining useful life of a component. Prognostic models may be data based, based on known physics of the system or can be hybrid, i.e., built through a combination of data and physics. To build such models, one needs either data from the field (i.e., real-world operations) or simulations/tests that qualitatively represent field observations. Often, field data is not easy to obtain and is limited in its availability. Thus, models are built with simulation or test data and then validated with field observations when they become available. This necessitates a procedure that allows for refinement of models to better represent real-world behavior without having to run expensive simulations or tests repeatedly. Further, a single prognostic model developed for an entire fleet may need to be updated with measurements obtained from individual units. In this paper, we describe a novel methodology, based on the Unscented Kalman Filter, that not only allows for updating such ``fleet" models, but also guarantees improvement over the existing model.

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Submission Keywords: 
Kalman Filtering
unscented Kalman filter
Model Updating
Submission Topic Areas: 
Component-level PHM
Industrial applications
Model-based methods for fault detection, diagnostics, and prognosis
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