NDLI: Predicting Time to Failure Using the IMM and Excitable Tests

Content Provider	IEEE Xplore Digital Library
Author	Phelps, E. Willett, P. Kirubarajan, T. Brideau, C.
Copyright Year	1996
Abstract	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.
Sponsorship	IEEE Systems, Man, and Cybernetics Society
Starting Page	630
Ending Page	642
Page Count	13
File Size	1305692
File Format	PDF
ISSN	10834427
Volume Number	37
Issue Number	5
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-09-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Sensor systems Fault diagnosis Engines Military aircraft System testing Trajectory Hypercubes Large-scale systems Condition monitoring Fault detection tracking fault detection interacting multiple model (IMM) Kalman filter prognostics
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Information Systems Electrical and Electronic Engineering Human-Computer Interaction Computer Science Applications Software

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