A Novel RSPF Approach to Prediction of High-Risk, Low-Probability Failure Events



Published Mar 26, 2021
Marcos E. Orchard Liang Tang Kai Goebel George Vachtsevanos


Particle filters (PF) have been established as the de facto state of the art in failure prognosis, and particularly in the representation and management of uncertainty in long-term predictions when used in combination with outer feedback correction loops. This paper presents a novel Risk- Sensitive PF (RSPF) framework that complements the benefits of the classic approach, by representing the probability of rare and costly events within the formulation of the nonlinear dynamic equation that describes the evolution of the fault condition in time. The performance of this approach is thoroughly compared using a set of proposed metrics for prognosis results. The scheme is illustrated with real vibration feature data from a fatigue-driven fault in a critical aircraft component.

How to Cite

E. Orchard, M., Tang, L., Goebel, K., & Vachtsevanos, G. (2021). A Novel RSPF Approach to Prediction of High-Risk, Low-Probability Failure Events. Annual Conference of the PHM Society, 1(1). Retrieved from https://papers.phmsociety.org/index.php/phmconf/article/view/1587
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particle filtering, prognostics, risk assessment, uncertainty management

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