Searching for just a few words should be enough to get started. If you need to make more complex queries, use the tips below to guide you.
Article type: Research Article
Authors: Bai, Xiaojuna | Jia, Haiyanga; * | Fu, Yanfanga | Ji, Yub | Li, Suyanga
Affiliations: [a] Xi’an Technological University, School of Computer Science and Engineering, Xi’an, China | [b] Xi'an Technological University, School of Mechatronic Engineering, Xi’an, China
Correspondence: [*] Corresponding author. Haiyang Jia, Xi’an Technological University, School of Computer Science and Engineering, Xi’an, China. Tel.: +86 17625656434; E-mail: [email protected].
Abstract: Predicting the remaining life of aircraft engines is paramount in aviation maintenance management. It helps formulate maintenance schedules, reduce maintenance expenses, and enhance flight safety. Traditional methods for predicting the remaining life of an engine suffer from significant errors and limited generalization capabilities. This paper introduces a predictive model based on Long Short-Term Memory (LSTM) networks and Feedforward Neural Networks (FNN) to improve prediction accuracy. Furthermore, the model’s hyperparameters undergo optimization using the Gannet Optimization Algorithm (GOA). Leveraging the N-CMAPSS dataset for prediction and transfer learning experiments, the results highlight the significant advantages of the proposed model in forecasting the remaining life of aircraft engines. When subjected to training and testing on the DS02 equipment dataset, the root mean square error (RMSE) registers at 5.04. At that time, the score function reached a value of 1.39, surpassing the performance of current state-of-the-art prediction methods. Additionally, in terms of its transfer learning capabilities, the model demonstrates minimal fluctuations in RMSE when applied directly to datasets of various other engine models. It consistently maintains a high level of predictive accuracy.
Keywords: Remaining life prediction, N-CMAPSS dataset, long short-term memory network, Gannet Optimization Algorithm (GOA)
DOI: 10.3233/JIFS-236225
Journal: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
USA
Tel: +1 703 830 6300
Fax: +1 703 830 2300
[email protected]
For editorial issues, like the status of your submitted paper or proposals, write to [email protected]
IOS Press
Nieuwe Hemweg 6B
1013 BG Amsterdam
The Netherlands
Tel: +31 20 688 3355
Fax: +31 20 687 0091
[email protected]
For editorial issues, permissions, book requests, submissions and proceedings, contact the Amsterdam office [email protected]
Inspirees International (China Office)
Ciyunsi Beili 207(CapitaLand), Bld 1, 7-901
100025, Beijing
China
Free service line: 400 661 8717
Fax: +86 10 8446 7947
[email protected]
For editorial issues, like the status of your submitted paper or proposals, write to [email protected]
如果您在出版方面需要帮助或有任何建, 件至: [email protected]