Probabilistic delamination diagnosis of composite materials using a novel Bayesian imaging method

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

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

1 Citation (Scopus)

Abstract

In this paper, a framework for probabilistic delamination location and size detection is proposed. A 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 presented. Following this, a fatigue testing setup for carbon-carbon composite coupons is introduced and the corresponding lamb wave based diagnostic signal is collected and interpreted. Next, the obtained signal features are incorporated in the Bayesian Imaging Method to detect delamination size and location, as along with corresponding uncertainty 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.

Original languageEnglish (US)
Title of host publicationPHM 2013 - Proceedings of the Annual Conference of the Prognostics and Health Management Society 2013
PublisherPrognostics and Health Management Society
Pages141-149
Number of pages9
ISBN (Print)9781936263066
StatePublished - 2013
Event2013 Annual Conference of the Prognostics and Health Management Society, PHM 2013 - New Orleans, United States
Duration: Oct 14 2013Oct 17 2013

Other

Other2013 Annual Conference of the Prognostics and Health Management Society, PHM 2013
CountryUnited States
CityNew Orleans
Period10/14/1310/17/13

Fingerprint

Bayes Theorem
Delamination
Carbon
Imaging techniques
Damage detection
Composite materials
Surface waves
Uncertainty
Fatigue
X-Rays
Carbon carbon composites
Fatigue testing
X rays

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Health Information Management
  • Software

Cite this

Peng, T., Saxena, A., Goebel, K., Sankararaman, S., Xiang, Y., & Liu, Y. (2013). Probabilistic delamination diagnosis of composite materials using a novel Bayesian imaging method. In PHM 2013 - Proceedings of the Annual Conference of the Prognostics and Health Management Society 2013 (pp. 141-149). Prognostics and Health Management Society.

Probabilistic delamination diagnosis of composite materials using a novel Bayesian imaging method. / Peng, Tishun; Saxena, Abhinav; Goebel, Kai; Sankararaman, Shankar; Xiang, Yibing; Liu, Yongming.

PHM 2013 - Proceedings of the Annual Conference of the Prognostics and Health Management Society 2013. Prognostics and Health Management Society, 2013. p. 141-149.

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

Peng, T, Saxena, A, Goebel, K, Sankararaman, S, Xiang, Y & Liu, Y 2013, Probabilistic delamination diagnosis of composite materials using a novel Bayesian imaging method. in PHM 2013 - Proceedings of the Annual Conference of the Prognostics and Health Management Society 2013. Prognostics and Health Management Society, pp. 141-149, 2013 Annual Conference of the Prognostics and Health Management Society, PHM 2013, New Orleans, United States, 10/14/13.
Peng T, Saxena A, Goebel K, Sankararaman S, Xiang Y, Liu Y. Probabilistic delamination diagnosis of composite materials using a novel Bayesian imaging method. In PHM 2013 - Proceedings of the Annual Conference of the Prognostics and Health Management Society 2013. Prognostics and Health Management Society. 2013. p. 141-149
Peng, Tishun ; Saxena, Abhinav ; Goebel, Kai ; Sankararaman, Shankar ; Xiang, Yibing ; Liu, Yongming. / Probabilistic delamination diagnosis of composite materials using a novel Bayesian imaging method. PHM 2013 - Proceedings of the Annual Conference of the Prognostics and Health Management Society 2013. Prognostics and Health Management Society, 2013. pp. 141-149
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