A transfer learning approach for acoustic emission zonal localization on steel plate-like structure using numerical simulation and unsupervised domain adaptation. (1st June 2023)

• Record Type: Journal Article
• Authors: Ai, Li; Zhang, Bin; Ziehl, Paul
• Is Part Of: Mechanical systems and signal processing. Volume 192(2023)
• Publication Date: 2023-06-01
• Subjects: Acoustic emission -- Finite element modeling -- Source localization -- Transfer learning -- Unsupervised domain adaptation -- Manifold embedded distribution alignment; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2023.110216
• Languages: English
• ISSNs: 0888-3270

Abstract

Abstract: The detection and localization of damage in metallic structures using acoustic emission (AE) monitoring and artificial intelligence technology such as deep learning has been widely studied. However, a current challenge of this approach is the difficulty of obtaining sufficient labeled historical AE signals for the training process of deep learning models. This problem can be approached through the implementation of transfer learning. The innovation of this paper lies in the development of a transfer learning approach for AE source localization on a stainless-steel structure when no historical labeled AE signals are available for training. A finite element model is developed to generate numerical AE signals for the training. Unsupervised domain adaptation (UDA) technology is utilized to reduce the distribution difference between the numerical and the realistic AE signals and to derive the localization results of the unlabeled realistic AE signals. The results suggest that the proposed approach is capable of localizing AE signals with high accuracy in the absence of labeled training data.