A Novel Way to Apply Transfer Learning to Aircraft System Fault Diagnosis

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Published Jun 29, 2022
Lilin Jia Cordelia Mattuvarkuzhali Ezhilarasu
Ian Jennions

Abstract

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

Jia, L., Ezhilarasu, C. M., & Jennions, I. (2022). A Novel Way to Apply Transfer Learning to Aircraft System Fault Diagnosis. PHM Society European Conference, 7(1), 577–579. https://doi.org/10.36001/phme.2022.v7i1.3299
Abstract 341 | PDF Downloads 97

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Keywords

fault diagnosis, transfer learning, aircraft systems, IVHM, condition-based maintenance

References
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Section
Doctoral Symposium