Probabilistic Delamination Diagnosis of Composite Materials Using a Novel Bayesian Imaging Method

Tishun Peng, Abhinav Saxena, Kai Goebel, Shankar Sankararaman, Yibing Xiang, and Yongming Liu
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Full Paper
phmc_13_030.pdf742.26 KBSeptember 16, 2013 - 1:34pm

In this paper, a probabilistic delamination location and size detection framework is proposed. The delamination probability image using Lamb wave-based damage detection is constructed using the Bayesian updating technique. First, the algorithm for the probabilistic delamination detection framework using Bayesian updating (Bayesian Imaging Method - BIM) is proposed. Following this, the composite coupon fatigue testing setup is introduced and the corresponding lamb wave diagnosis signal is collected and interpreted. Next, the obtained signal features are incorporated in the Bayesian Imaging Method to detect delamination size and location, as well as their confidence bounds. The damage detection results using the proposed methodology are compared with X-ray images for verification and validation. Finally, some conclusions and future works are drawn based on the proposed study.

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Submission Keywords: 
composite materials
Bayesian Imaging Method
Submission Topic Areas: 
Structural health monitoring
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