Quantifying the value of information transfer in population-based SHM
Aidan J. Hughes Jack Poole Nikolaos Dervilis Paul Gardner Keith Worden
Population-based structural health monitoring (PBSHM), seeks to address some
of the limitations associated with data scarcity that arise in traditional SHM.
A tenet of the population-based approach to SHM is that information can be
shared between sufficiently-similar structures in order to improve predictive
models. Transfer learning techniques, such as domain adaptation, have been
shown to be a highly-useful technology for sharing information between
structures when developing statistical classifiers for PBSHM. Nonetheless,
transfer-learning techniques are not without their pitfalls. In some
circumstances, for example if the data distributions associated with the
structures within a population are dissimilar, applying transfer-learning
methods can be detrimental to classification performance -- this phenomenon is
known as negative transfer. Given the potentially-severe consequences of
negative transfer, it is prudent for engineers to ask the question `when, what,
and how should one transfer between structures?'.
  The current paper aims to demonstrate a transfer-strategy decision process
for a classification task for a population of simulated structures in the
context of a representative SHM maintenance problem, supported by domain
adaptation. The transfer decision framework is based upon the concept of
expected value of information transfer. In order to compute the expected value
of information transfer, predictions must be made regarding the classification
(and decision performance) in the target domain following information transfer.
In order to forecast the outcome of transfers, a probabilistic regression is
used here to predict classification performance from a proxy for structural
similarity based on the modal assurance criterion.