Quantifying the value of positive transfer: An experimental case study
Abstract
In traditional approaches to structural health monitoring, challenges often arise associated with the availability of labelled data. Population-based structural health monitoring seeks to overcomes these challenges by leveraging data/information from similar structures via technologies such as transfer learning. The current paper demonstrate a methodology for quantifying the value of information transfer in the context of operation and maintenance decision-making. This demonstration, based on a population of laboratory-scale aircraft models, highlights the steps required to evaluate the expected value of information transfer including similarity assessment and prediction of transfer efficacy. Once evaluated for a given population, the value of information transfer can be used to optimise transfer-learning strategies for newly-acquired target domains.
Cite
@article{arxiv.2407.14342,
title = {Quantifying the value of positive transfer: An experimental case study},
author = {Aidan J. Hughes and Giulia Delo and Jack Poole and Nikolaos Dervilis and Keith Worden},
journal= {arXiv preprint arXiv:2407.14342},
year = {2024}
}
Comments
In Proceedings of the 11th European Workshop on Structural Health Monitoring (EWSHM 2024), Potsdam, Germany. July 2024. arXiv admin note: text overlap with arXiv:2311.03083