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Sample generation method for marine diesel engines based on FEM simulation and DCGAN

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Abstract

The healthy and stable operation of the ship’s power system is the foundation for the normal navigation of a ship. Data-driven ship power system condition monitoring is currently one of the research directions, but such methods often require a large amount of labeled data support. How to obtain a sufficient number of fault samples is the first problem to be solved for such methods. Therefore, a new fault sample generation scheme is proposed, which first uses the finite element method (FEM) to generate vibration data of marine diesel engines in different fault states, and uses deep convolutional generative adversarial network (DCGAN) to narrow the domain difference between simulation data and measured data, while retaining the fault characteristics of the simulation data, thereby generating synthetic fault data that is closer to the real fault state. The iteration number is determined through the comparison of time-domain, frequency-domain, loss function changes, and fault type identification results of synthetic data, measured data, and simulation data. The quality of the synthetic data is judged, and ultimately, a high-quality data sample for model training is generated.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grant No. 52271328). The authors are grateful to the editors and anonymous reviewers for their helpful comments and constructive suggestions.

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Authors and Affiliations

  1. School of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan, 430063, China

    Baoyue Li, Yonghua Yu, Weicheng Wang, Bingxin Cao, Defeng Xu & Yangfeng Yao

  2. Key Laboratory of Marine Power Engineering and Technology Under Ministry of Transport of People’s Republic of China, Wuhan University of Technology, Wuhan, 430063, China

    Yonghua Yu

Authors
  1. Baoyue Li
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  2. Yonghua Yu
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  3. Weicheng Wang
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  4. Bingxin Cao
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Corresponding author

Correspondence to Yonghua Yu.

Additional information

Baoyue Li is a doctoral student of Marine Engineering at Wuhan University of Technology. His main research interests are fault diagnosis and remaining life prediction of mechanical equipment.

Yonghua Yu is a Professor and Ph.D. supervisor in the School of Marine and Energy Power Engineering at Wuhan University of Technology. He received his Ph.D. in Marine Engineering from Wuhan University of Technology. His research interests include ship power system health management and intelligence, ship power system vibration and noise control technology

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Li, B., Yu, Y., Wang, W. et al. Sample generation method for marine diesel engines based on FEM simulation and DCGAN. J Mech Sci Technol 38, 2335–2345 (2024). https://doi.org/10.1007/s12206-024-0414-4

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  • DOI: https://doi.org/10.1007/s12206-024-0414-4

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