Luo, Kai and Zhu, Jiayin and Li, Zhenliang and Zhu, Huimin and Li, Ye and Hu, Runjiu and Fan, Tiankuo and Chang, Xiangqian and Zhuang, Long and Yang, Zhibo (2024) Ultrasonic Lamb Wave Damage Detection of CFRP Composites Using the Bayesian Neural Network. Journal of Nondestructive Evaluation, 43 (2). p. 48. ISSN 0195-9298, DOI https://doi.org/10.1007/s10921-024-01054-z.
Full text not available from this repository.Abstract
Composite plates are susceptible to various damages in complex conditions and working environments, which may reduce the reliability of the structure and threaten equipment and personal safety. Thus, the implementation of a robust online Structural health monitoring (SHM) system for these composite structures becomes imperative. To enhance reliability and safety, we introduce a robust online SHM system anchored by our newly developed damage detection Bayesian neural network (DD-BNN). The main contribution of this study lies in the DD-BNN to perform precise and reliable damage detection and localization in composite plates using only one actuator-receiver pair without any signal/feature pre-processing and human intervention. The proposed DD-BNN model innovatively combines probabilistic modeling with deep learning to address uncertainty in Lamb wave-based damage detection and model performance for composite plates, featuring a specialized probabilistic layer trained through Bayesian inference to efficiently encapsulate and manage uncertainty in model weights and activation. Notably, our method significantly simplifies the SHM system design and manual operation requirements. In addition, this approach not only reduces overfitting but also enhances robustness to noise, as confirmed by experiments on perturbation analysis of Gaussian and Poisson noise.
| Item Type: | Article |
|---|---|
| Funders: | National Natural Science Foundation of China (NSFC) |
| Uncontrolled Keywords: | Composite plates; Structural health monitoring; Damage detection; Lamb waves; Bayesian neural network; Deep learning |
| Subjects: | R Medicine T Technology > T Technology (General) |
| Divisions: | Faculty of Engineering > Biomedical Engineering Department |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 25 Sep 2024 07:43 |
| Last Modified: | 25 Sep 2024 07:43 |
| URI: | http://eprints.um.edu.my/id/eprint/45189 |
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