Quantifying the value of positive transfer: An experimental case study
This work addresses data scarcity in structural health monitoring for infrastructure maintenance, but it is incremental as it builds on existing transfer learning concepts.
The paper tackles the challenge of limited labeled data in structural health monitoring by proposing a method to quantify the value of information transfer from similar structures, demonstrated on laboratory-scale aircraft models to optimize transfer-learning strategies for new target domains.
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.