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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Bibliographic Details
Published in:	Applied intelligence (Dordrecht, Netherlands) Vol. 53; no. 19; pp. 22682 - 22699
Main Authors:	Wang, Youming, Wang, Yue
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.10.2023 Springer Nature B.V
Subjects:	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
Online Access:	Get full text
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