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DRN-GAN: an integrated deep learning-based health degradation assessment model for naval propulsion system

Jingtong Gao, Shaopeng Dong, Jin Cui, Mei Yuan, Juanru Zhao

Engineering Computations

ISSN: 0264-4401

Article publication date: 1 April 2022

Issue publication date: 7 June 2022

113

Abstract

Purpose

The purpose of this paper is to propose a new deep learning-based model to carry out better maintenance for naval propulsion system.

Design/methodology/approach

This model is constructed by integrating different deep learning algorithms. The basic idea is to change the connection structure of the deep neural network by introducing a residual module, to limit the prediction output to a reasonable range. Then, connect the Deep Residual Network (DRN) with a Generative Adversarial Network (GAN), which helps achieve data expansion during the training process to improve the accuracy of the assessment model.

Findings

Study results show that the proposed model achieves a better prediction effect on the dataset. The average performance and accuracy of the proposed model outperform the traditional models and the basic deep learning models tested in the paper.

Originality/value

The proposed model proved to be better performed naval propulsion system maintenance than the traditional models and the basic deep learning models. Therefore, our model may provide better maintenance advice for the naval propulsion system and will lead to a more reliable environment for offshore operations.

Keywords

Acknowledgements

This work was supported by the Beijing Natural Science Foundation under Grant L212033.

Citation

Gao, J., Dong, S., Cui, J., Yuan, M. and Zhao, J. (2022), "DRN-GAN: an integrated deep learning-based health degradation assessment model for naval propulsion system", Engineering Computations, Vol. 39 No. 6, pp. 2306-2325. https://doi.org/10.1108/EC-10-2021-0624

Publisher

:

Emerald Publishing Limited

Copyright © 2022, Emerald Publishing Limited

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