A new algorithm for prognostics using subset simulation

This work presents an efficient computational framework for prognostics by combining the particle filter-based prognostics principles with the technique of Subset Simulation, first developed in S.K. Au and J.L. Beck [Probabilistic Engrg. Mech., 16 (2001), pp. 263-277], which has been named PFP-SubSim. The idea behind PFP-SubSim algorithm is to split the multi-step-ahead predicted trajectories into multiple branches of selected samples at various stages of the process, which correspond to increasingly closer approximations of the critical threshold. Following theoretical development, discussion and an illustrative example to demonstrate its efficacy, we report on experience using the algorithm for making predictions for the end-of-life and remaining useful life in the challenging application of fatigue damage propagation of carbon-fibre composite coupons using structural health monitoring data. Results show that PFP-SubSim algorithm outperforms the traditional particle filter-based prognostics approach in terms of computational efficiency, while achieving the same, or better, measure of accuracy in the prognostics estimates. It is also shown that PFP-SubSim algorithm gets its highest efficiency when dealing with rare-event simulation

Particle filter-based delamination shape prediction in composites subjected to fatigue loading

Modeling generic size features of delamination, such as area or length, has long been considered in the literature for damage prognosis in composites through specific models describing damage state evolution with load cycles or time. However, the delamination shape has never been considered, despite that it holds important information for damage diagnosis and prognosis, including the delamination area, its center, and perimeter, useful for structural safety evaluation. In this context, this paper develops a novel particle filter (PF)-based framework for delamination shape prediction. To this end, the delamination image is discretized by a mesh, where control points are defined as intersections between the grid lines and the perimeter of the delamination. A parametric data-driven function maps each point position as a function of the load cycles and is initially fitted on a sample test. Then, a PF is independently implemented for each node whereby to predict their future positions along the mesh lines, thus allowing delamination shape progression estimates. The new framework is demonstrated with reference to experimental tests of fatigue delamination growth in composite panels with ultrasonics C-scan monitoring

Particle filter-based damage prognosis using online feature fusion and selection

Damage prognosis generally resorts to damage quantification functions and evolution models to quantify the current damage state and to predict the future states and the remaining useful life (RUL). The former typically consists of a function describing the relationship between the damage state and a statistical feature extracted from the measured signals, thus the prognostic performance will strongly depend on the selection of a proper feature. Given the best feature may vary for different specimens or even at each time instant for the same specimen during damage progression, such selection is a challenging task but has received little investigation so far. In this context, this paper proposes a particle filter-based damage prognosis framework, which involves an online feature fusion and selection scheme. A prognostic model is considered for each feature, with a multivariate process equation, formulated using both a damage degradation function and a bias parameter, and a measurement equation linking the damage state and that feature considering a data-driven model and the bias. One PF is used to estimate the damage state, its evolution parameters, and the bias for each model. Then, at each step, the feature with the smallest estimated bias is selected as the best feature providing the most likely state vectors and is used to select the most likely samples of the damage state and growth parameters for predicting the RUL and for calculating the prior at the next step. The proposed prognostic framework is demonstrated by an experimental study, where an aluminum lug structure subject to fatigue crack growth is monitored by a Lamb wave measurement system