In recent years, transfer learning as a method that solves many issues limiting the real-world application of conventional machine learning methods has received dramatically increasing attention in the field of machine fault diagnosis. One major finding from an initial literature review shows that the majority of the existing research only focus on the transfer of diagnostic knowledge between various conditions of the same machine or different representation of similar machines. The primary goal of the current work is to seek a way to apply transfer learning to distinct domains, thereby expanding the boundary of transfer learning in the fault diagnosis field. In particular, attempts will be made to explore ways of transferring knowledge between diagnostic tasks of different aircraft systems. One promising method to help achieving this goal is transfer learning by structural analogy, since this method is capable of extracting high-level structural knowledge to apply transfer learning between seemingly unrelated domains, similar to the scenarios of transfer between different aircraft systems.
How to Cite
fault diagnosis, transfer learning, aircraft systems, IVHM, condition-based maintenance
Deng, Y., Huang, D., Du, S., Li, G., Zhao, C., & Lv, J. (2021). A Double-Layer Attention Based Adversarial Network for Partial Transfer Learning in Machinery Fault Diagnosis. Computers in Industry, vol. 127. doi:10.1016/J.COMPIND.2021.103399
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Wang, H., & Yang, Q. (2011). Transfer Learning by Structural Analogy. Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence (513-518), August 7-11, San Francisco.
Yang, Q., Zhang, Y., Dai, W., & Pan, S. J. (2020). Transfer Learning. Cambridge: Cambridge University Press.
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