A Computationally-Efficient Inverse Approach to Probabilistic Strain-Based Damage Diagnosis

James E. Warner, Jacob D. Hochhalter, William P. Leser, Patrick E. Leser, and John A. Newman
Submission Type: 
Full Paper
phmc_16_010.pdf964.49 KBSeptember 6, 2016 - 5:23am

This work presents a computationally-efficient inverse approach to probabilistic damage diagnosis. Given strain data at a limited number of measurement locations, Bayesian inference and Markov Chain Monte Carlo (MCMC) sampling are used to estimate probability distributions of the unknown location, size, and orientation of damage. Substantial computational speedup is obtained by replacing a three-dimensional finite element (FE) model with an efficient surrogate model. The approach is experimentally validated on cracked test specimens where full field strains are determined using digital image correlation (DIC). Access to full field DIC data allows for testing of different hypothetical sensor arrangements, facilitating the study of strain-based diagnosis effectiveness as the distance between damage and measurement locations increases. The ability of the framework to effectively perform both probabilistic damage localization and characterization in cracked plates is demonstrated and the impact of measurement location on uncertainty in the predictions is shown. Furthermore, the analysis time to produce these predictions is orders of magnitude less than a baseline Bayesian approach with the FE method by utilizing surrogate modeling and effective numerical sampling approaches.

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Submission Keywords: 
Bayesian inference
surrogate modeling
damage diagnosis
Submission Topic Areas: 
Model-based methods for fault detection, diagnostics, and prognosis
Software health management
Structural health monitoring
Uncertainty Quantification and Management in PHM
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