A Novel RLS-KS Method for Parameter Estimation in Particle Filtering-Based Fatigue Crack Growth Prognostics

The accurate prognosis of fatigue crack growth (FCG) is vital for securing structural safety and developing maintenance plans. With the development of structural health monitoring (SHM) technology, the particle filter (PF) has been considered a promising tool for online prognostics of FCG. Among the existing FCG models, the traditional Paris-Erdogan model is most commonly used in PF-based FCG prognostics. The parameters of the Paris-Erdogan model can be estimated together with the crack state in the PF framework. However, we find that there is a problem of “Coordinated Change” when the parameters priors are far from the true values. As a result, the filtering results appear as a correct remaining useful life (RUL) prognosis but an incorrect parameters estimation. To solve this problem, in this paper, a novel recursive least squares-kernel smoothing (RLS-KS) method is proposed for parameter estimation in PF-based FCG prognostics. The proposed method is validated through an experimental application; and then compared with the classic artificial evolution (AE) and kernel smoothing (KS) methods. The validation results show that the RLS-KS method can provide both correct RUL prognostics and parameter estimation. Moreover, this method provides better performance for FCG prognostics compared with classic methods.