Structural health monitoring provides sensor data that monitor fatigue-induced damage growth in service. This information may in turn be used to improve the characterization of the material properties that govern damage propagation for the structure being monitored. These properties are often widely distributed between nominally identical structures because of differences in manufacturing processes and aging effects. The improved accuracy in damage growth characteristics allows more accurate prediction of the remaining useful life (RUL) of the structural component. In this paper, a probabilistic approach using Bayesian statistics is employed to progressively reduce the uncertainty in structure-specific damage growth parameters in spite of noise and bias in sensor measurements. Starting from an initial, wide distribution of damage parameters that are obtained from coupon tests, the distribution is progressively narrowed using the damage growth between consecutive measurements. Detailed discussions on how to construct the likelihood function under given noise of sensor data and how to update the distribution are presented. The approach is applied to crack growth in fuselage panels due to cycles of pressurization and depressurization. It is shown that the proposed method rapidly converges to the accurate damage parameters when the initial damage size is 20mm and there is no bias in the measurements. Fairly accurate material properties can be obtained also with measurement errors of 5mm. Using the identified damage parameters, it is shown that the 95% conservative RUL converges to the true RUL from the conservative side. The proposed approach may have the potential of turning aircraft into flying fatigue laboratories.
How to Cite
crack detection, damage detection, damage propagation model, fatigue crack growth, structural health management, structural health monitoring,, applications: aviation
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