On statistic alignment for domain adaptation in structural health monitoring

• Published in: Structural health monitoring Vol. 22; no. 3; pp. 1581 - 1600
• Main Authors: Poole, Jack, Gardner, Paul, Dervilis, Nikolaos, Bull, Lawrence, Worden, Keith
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.05.2023
• Subjects: Domain adaptation; transfer learning; deep learning; population–based structural health monitoring; machine learning; damage localisation
• ISSN: 1475-9217; 1741-3168
• DOI: 10.1177/14759217221110441

The practical application of structural health monitoring is often limited by the availability of labelled data. Transfer learning – specifically in the form of domain adaptation (DA) – gives rise to the possibility of leveraging information from a population of physical or numerical structures, by inferring a mapping that aligns the feature spaces. Typical DA methods rely on nonparametric distance metrics, which require sufficient data to perform density estimation. In addition, these methods can be prone to performance degradation under class imbalance. To address these issues, statistic alignment (SA) is discussed, with a demonstration of how these methods can be made robust to class imbalance, including a special case of class imbalance called a partial DA scenario. Statistic alignment is demonstrated to facilitate damage localisation with no target labels in a numerical case study, outperforming other state-of-the-art DA methods. It is then shown to be capable of aligning the feature spaces of a real heterogeneous population, the Z24 and KW51 bridges, with only 220 samples used from the KW51 Bridge. Finally, in scenarios where more complex mappings are required for knowledge transfer, SA is shown to be a vital pre-processing tool, increasing the performance of established DA methods.