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Proceedings Paper

Statistical approach to prognostics
Author(s): Ethan Phelps; Peter K. Willett; Thiagalingam Kirubarajan
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Paper Abstract

Prognostics, which refers to the inference of an expected time-to-failure for a mechanical system, is made difficult by the need to track and predict the trajectories of real-valued system parameters over essentially unbounded domains, and by the need to prescribe a subset of these domains in which an alarm should be raised. In this paper we propose a novel technique whereby these problems are avoided: instead of physical system or sensor parameters, sensor-level test-failure probability vectors (bounded within the unit hypercube) are tracked; and via a close relationship with the TEAMS suite of modeling tools, the terminal states for all such vectors can be enumerated. To perform the tracking, a Kalman filter with associated interacting multiple model switching between failure regimes is proposed, and simulation results indicate that performance is promising.

Paper Details

Date Published: 20 July 2001
PDF: 12 pages
Proc. SPIE 4389, Component and Systems Diagnostics, Prognosis, and Health Management, (20 July 2001); doi: 10.1117/12.434249
Show Author Affiliations
Ethan Phelps, Univ. of Connecticut (United States)
Peter K. Willett, Univ. of Connecticut (United States)
Thiagalingam Kirubarajan, Univ. of Connecticut (Canada)

Published in SPIE Proceedings Vol. 4389:
Component and Systems Diagnostics, Prognosis, and Health Management
Peter K. Willett; Thiagalingam Kirubarajan, Editor(s)

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