Predicting Time to Failure Using the IMM and Excitable Tests

Prognostics, which refers to the inference of an expected time to failure for a 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 an idea, one whereby these problems are avoided: Instead of physical system or sensor parameters, a vector corresponding to the failure probabilities of the system's sensors (which of course are bounded within the unit hypercube) is tracked. With the help of a system diagnosis model, the corresponding fault signatures can be identified as terminal states for these probability vectors. To perform tracking, Kalman filters and interacting multiple-model estimators are implemented for each sensor. The work that has been completed thus far shows promising results in both large-scale and small-scale systems, with the impending failures being detected quickly and the prediction of the time until this failure occurs being determined accurately.

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