A decision framework for selecting information-transfer strategies in population-based SHM
This work addresses data scarcity for structural health monitoring practitioners, but it is incremental as it builds on existing transfer learning techniques in population-based SHM.
The paper tackles the problem of data scarcity in structural health monitoring (SHM) by proposing a decision framework based on the expected value of information transfer to select transfer strategies, avoiding negative transfer and reducing costs and safety risks.
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.