Aero-Engine Remaining Useful Life Estimation Based on CAE-TCN Neural Networks

With the rapid growth of the aviation fields, the remaining useful life (RUL) estimation of aero-engine has become the focus of the industry. Due to the shortage of existing prediction methods, life prediction is stuck in a bottleneck. Aiming at the low efficiency of traditional estimation algorithm...

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Bibliographic Details
Published in:Applied sciences 2023-01, Vol.13 (1), p.17
Main Authors: Ren, Guanghao, Wang, Yun, Shi, Zhenyun, Zhang, Guigang, Jin, Feng, Wang, Jian
Format: Article
Language:English
Subjects:
aero-engine
Aeronautics
Aerospace engines
Algorithms
convolutional autoencoder
Data integration
Data processing
Efficiency
Life prediction
Long short-term memory
Modular systems
Multisensor fusion
Neural networks
Predictions
Preventive maintenance
Propulsion systems
remaining useful life estimation
temporal convolutional network
Time series
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Summary:With the rapid growth of the aviation fields, the remaining useful life (RUL) estimation of aero-engine has become the focus of the industry. Due to the shortage of existing prediction methods, life prediction is stuck in a bottleneck. Aiming at the low efficiency of traditional estimation algorithms, a more efficient neural network is proposed by using Convolutional Neural Networks (CNN) to replace Long-Short Term Memory (LSTM). Firstly, multi-sensor degenerate information fusion coding is realized with the convolutional autoencoder (CAE). Then, the temporal convolutional network (TCN) is applied to achieve efficient prediction with the obtained degradation code. It does not depend on the iteration along time, but learning the causality through a mask. Moreover, the data processing is improved to further improve the application efficiency of the algorithm. ExtraTreesClassifier is applied to recognize when the failure first develops. This step can not only assist labelling, but also realize feature filtering combined with tree model interpretation. For multiple operation conditions, new features are clustered by K-means++ to encode historical condition information. Finally, an experiment is carried out to evaluate the effectiveness on the Commercial Modular Aero-Propulsion System Simulation (CMAPSS) datasets provided by the National Aeronautics and Space Administration (NASA). The results show that the proposed algorithm can ensure high-precision prediction and effectively improve the efficiency.
ISSN:2076-3417
2076-3417
DOI:10.3390/app13010017