[RMR17a] Model-based prognosis using an explicit degradation model and Inverse FORM for uncertainty propagation
Conférence Internationale avec comité de lecture :
IFAC World Congress,
Mots clés: Inverse first-order reliability method ; remaining useful life ; model-based prognosis ; uncertainty propagation ; extended Kalman filter ; Paris’ law.
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.