A decision framework for selecting information-transfer strategies in population-based SHM

TL;DR

This paper introduces a decision framework for selecting transfer learning strategies in population-based structural health monitoring, aiming to improve decision-support while avoiding negative transfer and reducing maintenance costs.

Contribution

It proposes a novel expected value of information transfer concept to optimize transfer strategies and prevent negative transfer in SHM systems.

Findings

Framework effectively avoids negative transfer

Optimizes transfer strategies for cost reduction

Improves safety in structural maintenance

Abstract

Decision-support for the operation and maintenance of structures provides significant motivation for the development and implementation of structural health monitoring (SHM) systems. Unfortunately, the limited availability of labelled training data hinders the development of the statistical models on which these decision-support systems rely. Population-based SHM seeks to mitigate the impact of data scarcity by using transfer learning techniques to share information between individual structures within a population. The current paper proposes a decision framework for selecting transfer strategies based upon a novel concept -- the expected value of information transfer -- such that negative transfer is avoided. By avoiding negative transfer, and by optimising information transfer strategies using the transfer-decision framework, one can reduce the costs associated with operating and maintaining structures, and improve safety.