Statistical approach to prognostics

Ethan Phelps; Peter K. Willett; Thiagalingam Kirubarajan

doi:10.1117/12.434249

20 July 2001 Statistical approach to prognostics

Ethan Phelps, Peter K. Willett, Thiagalingam Kirubarajan

Proceedings Volume 4389, Component and Systems Diagnostics, Prognosis, and Health Management; (2001) https://doi.org/10.1117/12.434249
Event: Aerospace/Defense Sensing, Simulation, and Controls, 2001, Orlando, FL, United States

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.

Citation Download Citation

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

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