Reducing Uncertainty in Damage Growth Properties by Structural Health Monitoring

Alexandra Coppe, Raphael T. Haftka, Nam-Ho Kim, and Fuh-Gwo Yuan
Submission Type: 
Full Paper
Supporting Agencies (optional): 
NASA, Air Force
phmc_09_78.pdf628.25 KBSeptember 17, 2009 - 6:43am

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 narrow 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 are no errors in the measurements. Fairly accurate material properties can be obtained also with measurement errors of 5mm. Using the identified damage parameters, the RUL is predicted with 95% confidence in order to obtain conservative prediction. The proposed approach may have the potential of turning aircraft into flying fatigue laboratories.

Publication Control Number: 
Submission Keywords: 
applications: aviation
crack detection
damage detection
damage propagation model
fatigue crack growth
structural health management
structural health monitoring
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