TY - JOUR

T1 - Damage localisation using disparate damage states via domain adaptation

AU - Wickramarachchi, Chandula t.

AU - Gardner, Paul

AU - Poole, Jack

AU - Hübler, Clemens

AU - Jonscher, Clemens

AU - Rolfes, Raimund

N1 - Funding Information: We gratefully acknowledge the financial support of the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)—SFB-1463—434502799. C.T.W. would also like to thank the Mercator fellowship program as well as The Dynamics Research Group at the University of Sheffield for supporting this work.

PY - 2024/2/2

Y1 - 2024/2/2

N2 - A significant challenge of structural health monitoring (SHM) is the lack of labeled data collected from damage states. Consequently, the collected data can be incomplete, making it difficult to undertake machine learning tasks, to detect or predict the full range of damage states a structure may experience. Transfer learning is a helpful solution, where data from (source) structures containing damage labels can be used to transfer knowledge to (target) structures, for which damage labels do not exist. Machine learning models are then developed that generalize to the target structure. In practical applications, it is unlikely that the source and the target structures contain the same damage states or experience the same environmental and operational conditions, which can significantly impact the collected data. This is the first study to explore the possibility of transfer learning for damage localisation in SHM when the damage states and the environmental variations in the source and target datasets are disparate. Specifically, using several domain adaptation methods, this article localizes severe damage states at a target structure, using labeled information from minor damage states at a source structure. By minimizing the distance between the marginal and conditional distributions between the source and the target structures, this article successfully localizes damage states of disparate severities, under varying environmental and operational conditions. The effect of partial and universal domain adaptation - where the number of damage states in the source and target datasets differ - is also explored in order to mimic realistic industrial applications of these methods.

AB - A significant challenge of structural health monitoring (SHM) is the lack of labeled data collected from damage states. Consequently, the collected data can be incomplete, making it difficult to undertake machine learning tasks, to detect or predict the full range of damage states a structure may experience. Transfer learning is a helpful solution, where data from (source) structures containing damage labels can be used to transfer knowledge to (target) structures, for which damage labels do not exist. Machine learning models are then developed that generalize to the target structure. In practical applications, it is unlikely that the source and the target structures contain the same damage states or experience the same environmental and operational conditions, which can significantly impact the collected data. This is the first study to explore the possibility of transfer learning for damage localisation in SHM when the damage states and the environmental variations in the source and target datasets are disparate. Specifically, using several domain adaptation methods, this article localizes severe damage states at a target structure, using labeled information from minor damage states at a source structure. By minimizing the distance between the marginal and conditional distributions between the source and the target structures, this article successfully localizes damage states of disparate severities, under varying environmental and operational conditions. The effect of partial and universal domain adaptation - where the number of damage states in the source and target datasets differ - is also explored in order to mimic realistic industrial applications of these methods.

KW - PBSHM

KW - SHM

KW - damage localisation

KW - distance metrics

KW - domain adaptation

UR - http://www.scopus.com/inward/record.url?scp=85185222657&partnerID=8YFLogxK

U2 - 10.1017/dce.2023.29

DO - 10.1017/dce.2023.29

M3 - Article

VL - 5

JO - Data-Centric Engineering

JF - Data-Centric Engineering

IS - 1

M1 - e3

ER -