Quantifying the value of positive transfer: An experimental case study
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Abstract (arXiv)
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.
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References · 8
- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
- 2018Characterizing and Avoiding Negative Transfercited by 229
- 2017Pre-Processing-Free Gear Fault Diagnosis Using Small Datasets with Deep Convolutional Neural Network-Based Transfer Learningcited by 17
- 2021Foundations of Population-Based SHM, Part IV: The Geometry of Spaces of Structures and their Feature Spacescited by 15
- 2022On robust risk-based active-learning algorithms for enhanced decision supportcited by 8
- 2022Zero-Shot Transfer Learning for Structural Health Monitoring using Generative Adversarial Networks and Spectral Mappingcited by 6
- 2023On the hierarchical Bayesian modelling of frequency response functionscited by 6
- 2023Quantifying the value of information transfer in population-based SHMcited by 3
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