Performance qualification of an on-board model-based diagnostic system for fatigue crack monitoring

Very little success has been reported in the literature in developing diagnostic systems trained on simulated data that can accurately describe real situations. Furthermore, very few studies attempting distributed structural health monitoring (SHM) system performance qualification are available. A diagnostic algorithm based on an artificial neural network and trained with finite element simulated strains has been verified during repeated fatigue crack growth tests on metallic helicopter fuselage panels. Strain measures from a network of fiber Bragg gratings are provided as input to the diagnostic system, allowing fatigue crack damage identification. Anomaly detection performances have been evaluated with reference to the recent Aerospace Recommended Practice (ARP-6461) and the Recommended Practice for a Demonstration of Non Destructive Evaluation Reliability on Aircraft Production Parts, providing a SHM system qualification in terms of minimum detectable crack length, based on reliability-confidence curves. Furthermore, the numericalmodel of themonitored structure has been used for the generation of virtual specimens, thus predicting system performances in a model-assisted framework.