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An interacting multiple model approach to model-based prognostics

Authors: Jianhui Luo; Andrew Bixby; Krishna R. Pattipati; Liu Qiao; Masayuki Kawamoto; Shunsuke Chigusa;

An interacting multiple model approach to model-based prognostics

Abstract

A system wide prognostic process is required to fulfill the needs of expensive and high availability industrial systems. The recent advances in model-based design technology have facilitated the integration of model-based diagnosis and prognosis of systems, leading to condition-based maintenance. In this paper an integrated prognostic process based on data collected from model-based simulations under nominal and degraded conditions is described. Interacting Multiple Model (IMM) is used to track the hidden damage. Remaining life prediction is performed by mixing mode-based life predictions via time-averaged mode probabilities. The prognostic process is demonstrated on a suspension system.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
5
Average
Top 10%
Average
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