Numerical simulation-aided particle filter-based damage prognosis using Lamb waves

Damage prognosis generally resorts to a damage evolution model and the current damage state to predict the future state and the remaining useful life (RUL). As a properly defined damage-sensitive statistical feature extracted from the Lamb waves can be used to online quantify the damage state, it is here exploited within a particle filter (PF) scheme to perform damage prognosis in structural health monitoring. An accurate mapping between the damage state and this feature would require a sufficient set of experimental Lamb waves collected in correspondence of different damage levels occurring during the run-to-failure process, which, however, is not usually available in real practice due to the high costs and the complex logistics involved in such experimental campaigns. In order to deal with this issue, this paper develops a new numerical simulation-aided particle filter-based damage prognosis framework, where the process equation is still built on the basis of available physical knowledge about the degradation process, whereas the measurement equation is built by means of a data-driven modeling approach using the features extracted from the numerically simulated Lamb waves. The PF framework serves as the state estimation tool which allows to identify the damage state and the parameters in the two equations. The future damage states and the RUL can finally be predicted by projecting the PF estimates in the future using the process equation. The proposed framework is demonstrated with reference to experimental studies of fatigue crack growth in aluminum lug structures with online Lamb wave monitoring.