@inproceedings{465a2cfa54f84f7e801377c9821f6878,
title = "Aircraft Control Surface Fault Stepwise Diagnosis Method Based on Deep Learning",
abstract = "The fault diagnosis system for aircraft control surface is significant for f light safety. In this paper, an aircraft control surface fault stepwise diagnosis (FSD) method based on deep learning is proposed. The fault type, location and degree are step-by-step diagnosed. The fault feature parameter extraction (FFPE) equations that best reflect specific faults feature are extracted based on flight dynamics analysis, and the input and output of the neural network are determined. The fault rule base is established based on the multiple Long Short Term Memory (LSTM) classification neural network to detect the fault type and location respectively. Then the fault feature parameter equations are simplified and calculated to obtain the fault degree. The results of simulations validate the effectiveness and superiority of the FSD method compared with other methods.",
keywords = "Control Surface, Fault Feature Parameter Extraction, Fault Stepwise Diagnosis, LSTM",
author = "Jin Wang and Shang Tai and Lixin Wang and Ting Yue and Hailiang Liu and Jinhua Zhang",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.; International Conference on Guidance, Navigation and Control, ICGNC 2024 ; Conference date: 09-08-2024 Through 11-08-2024",
year = "2025",
doi = "10.1007/978-981-96-2208-5\_8",
language = "英语",
isbn = "9789819622078",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "81--90",
editor = "Liang Yan and Haibin Duan and Yimin Deng",
booktitle = "Advances in Guidance, Navigation and Control - Proceedings of 2024 International Conference on Guidance, Navigation and Control Volume 3",
address = "德国",
}