Quantifying the value of positive transfer: An experimental case study

TL;DR

This paper presents a methodology to quantify the benefits of transfer learning in structural health monitoring, using laboratory aircraft models to improve decision-making and optimize transfer strategies.

Contribution

It introduces a systematic approach to evaluate the value of information transfer, including similarity assessment and transfer efficacy prediction, for structural health monitoring.

Findings

Demonstrates the methodology on laboratory aircraft models

Highlights steps for evaluating transfer value

Shows potential for optimizing transfer-learning strategies

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.