A denoising semi-supervised deep learning model for remaining useful life prediction of turbofan engine degradation

Remaining useful life (RUL) prediction is significant for reliability analysis and the reduction of maintenance costs for turbofan engine systems. However, most of the existing methods capture temporal or spatial features to predict RUL, which leads to the neglect of deep spatio-temporal correlation...

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Vydáno v:Applied intelligence (Dordrecht, Netherlands) Ročník 53; číslo 19; s. 22682 - 22699
Hlavní autoři: Wang, Youming, Wang, Yue
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.10.2023
Springer Nature B.V
Témata:
Artificial Intelligence
Artificial neural networks
Computer Science
Cost analysis
Deep learning
Feature extraction
Life prediction
Machine learning
Machines
Maintenance costs
Manufacturing
Mechanical Engineering
Multilayers
Noise reduction
Prediction models
Processes
Reliability analysis
Root-mean-square errors
Semi-supervised learning
Turbofan engines
Useful life
Long short-term memory network
Denoising autoencoder
Remaining useful life
Semi-supervised deep learning
Convolutional neural network
ISSN:0924-669X, 1573-7497
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Shrnutí:Remaining useful life (RUL) prediction is significant for reliability analysis and the reduction of maintenance costs for turbofan engine systems. However, most of the existing methods capture temporal or spatial features to predict RUL, which leads to the neglect of deep spatio-temporal correlation in the prediction data. In addition, there are usually noise interference and few labels in feature extraction, which causes difficulty in designing robust RUL prediction models. To address these problems, a denoised semi-supervised model based on fully convolutional denoising autoencoder, convolutional neural network, and long short-term memory network (FCDAE-CNN-LSTM) is proposed to predict RUL, where FCDAE is constructed to denoise the original data by parameter fine-tuning and CNN-LSTM is established based on parallel connection for RUL prognosis. The denoising and generalization capabilities of the RUL prediction model are enhanced by the combination of unsupervised denoising and supervised feature extraction in the case of few labeled training data. The superior features of the method lie in that FCDAE effectively captures globalized and localized features and CNN-LSTM captures multi-layer fused spatial–temporal correlations. The Root Mean Square Error (RMSE) and Score of the proposed method on C-MAPSS dataset are 12.01, 16.62, 11.84, 18.21, and 209.44, 1466.03, 205.07, 2338.93, respectively, which have demonstrated that our method achieves the state-of-the-art performance and outperforms other models.
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ISSN:0924-669X
1573-7497
DOI:10.1007/s10489-023-04777-0