Impact of Input Uncertainty on Failure Prognostic Algorithms: Extending the Remaining Useful Life of Nonlinear Systems

Shared by Miryam Strautkalns, updated on Jun 19, 2013


Author(s) :
J. Liu, A. Saxena, K. Goebel, B. Saha, W. Wang

This paper presents a novel set of uncertainty measures to quantify the impact of input uncertainty on nonlinear prognosis systems. A Particle Filtering-based method is also presented that uses this set of uncertainty measures to quantify, in real time, the impact of load, environmen- tal, and other stresses for long-term prediction. Further- more, this work shows how these measures can be used to implement a novel feedback correction loop aimed to suggest modifications, at a system input level, with the purpose of extending the remaining useful life of a faulty nonlinear, non-Gaussian system. The correction scheme is tested and illustrated using real vibration feature data from a fatigue-driven fault in a critical aircraft compo- nent.

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