Model-based prognosis using an explicit degradation model and Inverse FORM for uncertainty propagation

Elinirina Robinson (1) , Julien Marzat (1) , Tarek Raissi (2)

1 DTIS, ONERA, Université Paris Saclay (COmUE) [Palaiseau]
2 CEDRIC - LAETITIA - CEDRIC. Traitement du signal et architectures électroniques

In this paper, an analytical method issued from the field of reliability analysis is used for prognosis. The inverse first-order reliability method (Inverse FORM) is an uncertainty propagation method that can be adapted to remaining useful life (RUL) calculation. An extended Kalman filter (EKF) is first applied to estimate the current degradation state of the system, then the Inverse FORM allows to compute the probability density function (pdf) of the RUL. In the proposed Inverse FORM methodology, an analytical or numerical solution to the differential equation that describes the evolution of the system degradation is required to calculate the RUL model. In this work, the method is applied to a Paris fatigue crack growth model, and then compared to filter-based methods such as EKF and particle filter using performance evaluation metrics (precision, accuracy and timeliness). The main advantage of the Inverse FORM is its ability to compute the pdf of the RUL at a lower computational cost.

Mots clés: uncertainty propagation; model-based prognosis; Paris' law; inverse first-order reliability method (Inverse FORM); remaining useful life (RUL); extended Kalman filter

Domaines: Automatique / Robotique

Elinirina Robinson, Julien Marzat, Tarek Raissi. Model-based prognosis using an explicit degradation model and Inverse FORM for uncertainty propagation. 20th IFAC World Congress, Jul 2018, Toulouse, France. pp.14242 - 14247, ⟨10.1016/j.ifacol.2017.08.1815⟩. ⟨hal-02470327⟩