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

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. INTRODUCTION Structural health monitoring (SHM) systems provide a means of augmenting operation and maintenance decision processes with up-to-date information regarding the health-state of a structure or system [1]. In order to assign features extracted from sensor data to meaningful categories in the context of the decision process (e.g.