Authors
Ariane Lorton, Mitra Fouladirad, Antoine Grall,
Title
A methodology for probabilistic model-based prognosis
In
European Journal of Operational Research
Volume
225
Issue
3
Pages
443–454
Publisher
ScienceDirect
Year
2013
Publisher's URL
http://www.sciencedirect.com/science/article/pii/S0377221712007710
Indexed by
Abstract
This paper deals with the prognosis of complex systems using stochastic model-based techniques. Prognosis consists in this case in computing the distribution of the Remaining Useful Life (RUL) of the system conditionally to available information. In so doing, three main challenges arise from the industrial context. First, the model should unify the two classical approaches to describing complex systems: the bottom-up and the top-down approaches. The former uses elementary interacting components whilst the latter models the system’s physical behavior by means of a set of differential equations. Second, the prognosis must integrate online information to provide a specific result for each system depending on their life events. Online information can take different forms (e.g. inspections, component faults, non detection or false alarm, noisy signal) which must all be considered. Third, the prognosis must supply ready, meaningful numerical results, the error of which must also be under control. This paper proposes a method addressing those challenges. The method is illustrated with two different examples: a simplified spring-mass system and a pneumatic valve for aeronautical application.
Affiliations
Offprint