Special Topics in Fault Diagnosis
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 4
1001-1007

Remaining useful life prediction of turbofan engines based on feature enhancement and spatio-temporal information embedding

Li Yongcheng
Li Wenxiao
Lei Yinjie
College of Electronics & Information Engineering, Sichuan University, Chengdu 610065, China
DOI: 10.19734/j.issn.1001-3695.2023.08.0364

Abstract

To address the low utilization of raw data and insufficient feature extraction capability of multi-dimensional data in existing remaining useful life prediction methods, this paper proposed a convolutional neural network model based on feature enhancement and spatio-temporal information embedding. Firstly, it adopted a feature enhancement module to extract additional operating condition features and manual features from raw data as auxiliary features. Then, it introduced the spatio-temporal embedding module to encode the spatio-temporal information, embedding the time series information and spatial feature information into the original data. Finally, it concatenated the aforementioned features, and it employed a regression prediction module to capture the inherent relationships in the data and obtain regression prediction results. It evaluated the predictive effectiveness of the proposed model on the commonly used commercial modular aero-propulsion system simulation(C-MAPSS) dataset. The experimental results show that the root mean square error of the proposed model decreases by 8.8% on average over the four subsets compared with other mainstream deep learning methods, and it also outperforms existing state-of-the-art algorithms in prediction accuracy under multiple operating conditions and fault types. The experiments fully verify the effectiveness and accuracy of the proposed model in predicting the remaining useful life of turbofan engines.

Foundation Support

装发预研项目

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.08.0364
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 4
Section: Special Topics in Fault Diagnosis
Pages: 1001-1007
Serial Number: 1001-3695(2024)04-006-1001-07

Publish History

[2023-11-02] Accepted Paper
[2024-04-05] Printed Article

Cite This Article

李勇成, 李文骁, 雷印杰. 基于特征增强与时空信息嵌入的涡扇发动机剩余寿命预测 [J]. 计算机应用研究, 2024, 41 (4): 1001-1007. (Li Yongcheng, Li Wenxiao, Lei Yinjie. Remaining useful life prediction of turbofan engines based on feature enhancement and spatio-temporal information embedding [J]. Application Research of Computers, 2024, 41 (4): 1001-1007. )

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Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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