20 July 2001 Statistical approach to prognostics
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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.
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Ethan Phelps, Ethan Phelps, Peter K. Willett, Peter K. Willett, Thiagalingam Kirubarajan, Thiagalingam Kirubarajan, } "Statistical approach to prognostics", Proc. SPIE 4389, Component and Systems Diagnostics, Prognosis, and Health Management, (20 July 2001); doi: 10.1117/12.434249; https://doi.org/10.1117/12.434249
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