&lt;title&gt;Useful lifetime tracking via the IMM&lt;/title&gt;

Phelps, Ethan; Willett, Peter; Kirubarajan, Thiagalingam

doi:10.1117/12.475504

Cited by 5 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Then, in Section 3 there will be a review of tracking algorithms [Ph01], [Ph02] and models. In Section 4 we describe how prognostic inference (time to failure and prediction of failure type) are to overlay the trackers, followed by a description of how the algorithms were tested.…”

Section: Proposed Approachmentioning

confidence: 99%

Predicting Time to Failure Using the IMM and Excitable Tests

Phelps

Willett

Kirubarajan

et al. 2007

IEEE Trans. Syst., Man, Cybern. A

Self Cite

View full text Add to dashboard Cite

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 the fault signatures can be identified as terminal states for these probability vectors. To perform the tracking, Kalman filters and interacting multiple model (IMM) estimators are implemented for each sensor. The work that has been completed thus far shows promising results in both large and small-scale systems, with the impending failures being detected quickly and the prediction of the time until this failure occurs being determined accurately.

show abstract

Section: Proposed Approachmentioning

confidence: 99%