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

Derek Edwards, Marcos Orchard, Liang Tang, Kai Goebel, and George Vachtsevanos
Submission Type: 
Full Paper
phmc_10_058.pdf545.35 KBOctober 6, 2010 - 11:49am

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, environmental, and other stresses for long-term prediction. Furthermore, 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 component.

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Submission Keywords: 
remaining useful life (RUL)
nonlinear systems
uncertainty management
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