Support Vector Machine and Artificial Neural Network in Aircraft Engine Fault Diagnosis

DOI

10.2991/978-94-6463-864-6_65How to use a DOI?

Keywords

Aircraft Engine Fault Diagnosis; Support Vector Machine (Svm); Artificial Neural Network (Ann); Prognostics And Health Management (Phm); Data-Driven Diagnosis

Abstract

Nowadays, the aviation industry plays an important role in the development of the country, while the need for accurate prediction of aircraft engines is increasing. Among the numerous prediction methods and models, the Support Vector Machine Method and the Artificial Neural Network Method, which will be described in this essay, are capable of making relatively accurate predictions under various circumstances based on different principles. Following a comparative analysis, it can be concluded that the support vector machine (SVM) generally outperforms the artificial neural network (ANN) in terms of diagnostic accuracy when dealing with small sample sizes. However, if the ANN is appropriately trained and optimised, its robust learning capabilities may yield superior diagnostic results when a sufficient sample size is available. Although the experiments and applications of these two methods are not yet mature at present, they can be improved and widely applied through optimisation methods in the future, which can effectively reduce maintenance costs and enhance the reliability of aircraft.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Yinuo Yang
PY  - 2025
DA  - 2025/10/23
TI  - Support Vector Machine and Artificial Neural Network in Aircraft Engine Fault Diagnosis
BT  - Proceedings of the 2025 2nd International Conference on Electrical Engineering and Intelligent Control (EEIC 2025)
PB  - Atlantis Press
SP  - 765
EP  - 774
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-864-6_65
DO  - 10.2991/978-94-6463-864-6_65
ID  - Yang2025
ER  -