Prediction of remaining useful life using fused deep learning models - a case study of turbofan engines

Zheng, Yu, Bao, Xiangyu, Zhao, Fei, Chen, Chong, Liu, Ying ORCID: https://orcid.org/0000-0001-9319-5940, Sun, Bo and Wang, Haotong 2022. Prediction of remaining useful life using fused deep learning models - a case study of turbofan engines. Journal of Computing and Information Science in Engineering 22 (5) , 054501. 10.1115/1.4054090

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Abstract

The study of intelligent operation and maintenance methods for turbofan engines is of great importance for improving the reliability of turbofan engines. Given the harsh operating conditions and complex structure of the turbofan engine, it is extremely difficult to establish an accurate physical model for remaining useful life (RUL) prediction. The traditional operation and maintenance method based on the physical model has several limitations in the application of turbofan engines, while the data-driven method offers a new solution. Compared with traditional machine learning models, deep learning models possess more powerful non-linear expression capabilities and feature extraction capabilities. Therefore, this work focuses on studying the RUL prediction algorithm for turbofan engines based on the fused deep learning models. In this paper, a multi-modal deep learning approach based on a 1DCNN (1D convolutional neural network) + attention enhanced Bi-LSTM (Bi-directional Long Short-Term Memory) network is proposed to predict the RUL by mining the temporal information of data. Furthermore, a DDResNet (dilated deep residual network) is also introduced to the 1DCNN submodel to leverage its hidden pattern mining capability due to its advance in preventing performance degradation across layers. Subsequently, the output of these two sub-models is weighted fused to obtain the final RUL prediction. The merits of the proposed method are demonstrated by comparing it with existing methods for RUL prediction using the C-MAPSS dataset.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
ISSN:	1530-9827
Date of First Compliant Deposit:	22 March 2022
Date of Acceptance:	15 February 2022
Last Modified:	10 Nov 2022 10:54
URI:	https://orca.cardiff.ac.uk/id/eprint/148594

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