Overcoming the problem of repair in structural health monitoring: Metric-informed transfer learning

• Published in: Journal of sound and vibration Vol. 510; p. 116245
• Main Authors: Gardner, P., Bull, L.A., Dervilis, N., Worden, K.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Ltd 13-10-2021; Elsevier Science Ltd
• Subjects: Adaptation; Algorithms; Chemical plants; Domain adaptation; Domains; Industrial applications; Learning; Machine learning; Physical properties; Population-based structural health monitoring; Repair; Structural health monitoring; Training aircraft; Transfer learning; Domain adaptation; Transfer learning; Population-based structural health monitoring
• ISSN: 0022-460X; 1095-8568
• DOI: 10.1016/j.jsv.2021.116245

Structural repairs alter the physical properties of a structure, changing its responses, both in terms of its normal condition and of its different damage states. This difference in responses manifests itself as a shift between the pre- and post-repair data distributions, which can be problematic for conventional data-driven approaches to structural health monitoring (SHM), and limits their effectiveness in industrial applications. This limitation occurs typically because approaches assume that the data distribution is the same in training as appears in testing; with an algorithm failing to generalise when this assumption is not true; that is, pre-repair labels no longer apply to the post-repair data. Transfer learning, in the form of domain adaptation, proposes a solution to this issue, by mapping the pre- and post-repair data distributions onto a shared latent space where their distributions are approximately equal, allowing pre-repair label knowledge to be used to classify the post-repair data. This paper demonstrates the applicability of domain adaptation as a method for overcoming the problem of repair on a dataset from a Gnat trainer aircraft. In addition, a novel modification to an existing domain adaptation technique – joint distribution adaptation – is proposed, which seeks to improve the semi-supervised learning phase of the algorithm by considering a metric-informed procedure. The metric-informed joint distribution adaptation algorithm is benchmarked against, and shown to outperform, both conventional data-based approaches and other domain adaptation techniques.
•Proposes, for the first time, the application of domain adaptation to overcome the problem of repair in data-based structural health monitoring.•A novel modification to joint domain adaptation, named metric-informed joint domain adaptation, improves initial pseudo-label estimates for the target dataset.•Demonstrates existing and proposed domain adaptation techniques on a repair problem for a Gnat trainer aircraft wing.