A recursive Bayesian approach for fatigue damage prognosis: An experimental validation at the reliability component level

Abstract Fatigue-induced damage is one of the most uncertain and highly unpredictable failure mechanisms for a large variety of mechanical and structural systems subjected to cyclic and random loads during their service life. A health monitoring system capable of (i) monitoring the critical components of these systems through non-destructive evaluation (NDE) techniques, (ii) assessing their structural integrity, (iii) recursively predicting their remaining fatigue life (RFL), and (iv) providing a cost-efficient reliability-based inspection and maintenance plan (RBIM) is therefore ultimately needed. In contribution to these objectives, the first part of the paper provides an overview and extension of a comprehensive reliability-based fatigue damage prognosis methodology — previously developed by the authors — for recursively predicting and updating the RFL of critical structural components and/or sub-components in aerospace structures. In the second part of the paper, a set of experimental fatigue test data, available in the literature, is used to provide a numerical verification and an experimental validation of the proposed framework at the reliability component level (i.e., single damage mechanism evolving at a single damage location). The results obtained from this study demonstrate (i) the importance and the benefits of a nearly continuous NDE monitoring system, (ii) the efficiency of the recursive Bayesian updating scheme, and (iii) the robustness of the proposed framework in recursively updating and improving the RFL estimations. This study also demonstrates that the proposed methodology can lead to either an extent of the RFL (with a consequent economical gain without compromising the minimum safety requirements) or an increase of safety by detecting a premature fault and therefore avoiding a very costly catastrophic failure.