Fatigue crack growth estimation by relevance vector machine

The investigation of damage propagation mechanisms on a selected safety–critical component or structure requires the quantification of its remaining useful life (RUL) to verify until when it can continue performing the required function. In this work, a relevance vector machine (RVM), that is a Bayesian elaboration of support vector machine (SVM), automatically selects a low number of significant basis functions, called relevant vectors (RVs), for degradation model identification, degradation state regression and RUL estimation. In particular, RVM capabilities are exploited to provide estimates of the RUL of a component undergoing crack growth, within an original combination of data-driven and model-based approaches to prognostics. The application to a case study shows that the proposed approach compares well to other methods (the model-based Bayesian approach of particle filtering and the data-driven fuzzy similarity-based approach) with respect to computational demand, data requirements, accuracy and that its Bayesian setting allows representing and propagating the uncertainty in the estimates.

► We use relevance vector machines and model fitting to compute residual life. ► The approach hybridize data-driven and model-based approaches for prognostics. ► The approach is applied to a component undergoing fatigue crack growth. ► The proposed approach outperforms other data-driven and model-based methods. ► The approach allows for the adequate representation of the estimates uncertainty.