[RMR16] Model-based prognosis algorithms with uncertainty propagation: application to a fatigue crack growth

Conférence Internationale avec comité de lecture : 3rd Conference on Control and Fault-Tolerant Systems (SysTol'16), September 2016, pp.., Barcelona, Spain,

Auteurs: E. Robinson , J. Marzat , T. RaÃ¯ssi

Mots clés: Prognosis ; health monitoring; uncertainties

Résumé: In this paper, deterministic and stochastic nonlin- ear prognosis methods that take uncertainty propagation into account are evaluated. More specifically, a deterministic method using interval techniques and two stochastic methods based on Bayesian filtering, namely extended Kalman filter and particle filter, are considered. The three algorithms are compared with reference to a classical benchmark which is a crack growth analysis, however they can be extended to other applications as well. The advantages and drawbacks of each approach are studied through different prognosis metrics such as accuracy, precision and timeliness. Based on these numerical simulations, the results show that deterministic methods can appropriately manage uncertainty for prognosis.

BibTeX

@inproceedings {
	RMR16,
	title	=	"{Model-based prognosis algorithms with uncertainty propagation: application to a fatigue crack growth}",
	author	=	" E. Robinson and J. MARZAT and T. RaÃ¯ssi ",
	booktitle	=	"{3rd Conference on Control and Fault-Tolerant Systems (SysTol'16)}",
	year	=	2016,
	month	=	"September",
	pages	=	".",
	address	=	"Barcelona, Spain",
}