Probabilistic damage diagnosis of composite laminates using Bayesian inference

Tishun Peng, Abhinav Saxena, Kai Goebel, Yibing Xiang, Yongming Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

In this paper, a non-deterministic 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 and size location detection framework using Bayesian updating (Bayesian Imaging Method - BIM) is developed. Following this, a composite coupon fatigue testing setup is given and the corresponding lamb wave diagnosis signal is collected and interpreted. Next, the obtained signal features are incorporated in the Bayesian Imaging Method for the delamination size and location, as well as their confidence bounds, detection. 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.

Original languageEnglish (US)
Title of host publication16th AIAA Non-Deterministic Approaches Conference
StatePublished - 2014
Event16th AIAA Non-Deterministic Approaches Conference - SciTech Forum and Exposition 2014 - National Harbor, MD, United States
Duration: Jan 13 2014Jan 17 2014

Other

Other16th AIAA Non-Deterministic Approaches Conference - SciTech Forum and Exposition 2014
CountryUnited States
CityNational Harbor, MD
Period1/13/141/17/14

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Mechanics of Materials
  • Building and Construction
  • Architecture

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