Journal of the Society of Naval Architects of Korea (대한조선학회논문집)

The Society of Naval Architects of Korea (대한조선학회)
권호 표지
DOI QR코드

DOI QR Code

A Study on the Risk of Propeller Cavitation Erosion Using Convolutional Neural Network

합성곱 신경망을 이용한 프로펠러 캐비테이션 침식 위험도 연구

  • Kim, Ji-Hye (Department of Naval Architecture and Marine Engineering, Changwon National University) ;
  • Lee, Hyoungseok (Ship Performance Research Department, Hyundai Maritime Research Institute, Hyundai Heavy Industries) ;
  • Hur, Jea-Wook (Ship Performance Research Department, Hyundai Maritime Research Institute, Hyundai Heavy Industries)
  • 김지혜 (창원대학교 조선해양공학과) ;
  • 이형석 (현대중공업 선박해양연구소 선박성능연구실) ;
  • 허재욱 (현대중공업 선박해양연구소 선박성능연구실)
  • Received : 2020.12.01
  • Accepted : 2021.02.23
  • Published : 2021.06.20
Download PDF
⟨ Previous Next ⟩

Abstract

Cavitation erosion is one of the major factors causing damage by lowering the structural strength of the marine propeller and the risk of it has been qualitatively evaluated by each institution with their own criteria based on the experiences. In this study, in order to quantitatively evaluate the risk of cavitation erosion on the propeller, we implement a deep learning algorithm based on a convolutional neural network. We train and verify it using the model tests results, including cavitation characteristics of various ship types. Here, we adopt the validated well-known networks such as VGG, GoogLeNet, and ResNet, and the results are compared with the expert's qualitative prediction results to confirm the feasibility of the prediction algorithm using a convolutional neural network.

Keywords

References

  1. Alex, K., Ilya, S. & Geoffrey, E. H., 2012. ImageNet classification with deep convolutional neural networks, Advances in Neural Information Processing System, pp.1097-1105.
  2. Chao, Q., Tao, J., Wei, X., Wang, Y., Meng, L. & Liu, C., 2020. Cavitation Intensity recognition for high-speed axial piston pumps using 1-D convolutional neural networks with multi-channel inputs of vibration signals. Alexandria Engineering Journal, pp. 4463-4473.
  3. Chollet, F., 2017. Deep learning with python translated by park,H. S. Manning Publications: New York.
  4. He, K., Zhang, X., Ren, S. & Sun, J., 2016. Deep residual learning for image recognition, IEEE conference on computer vision and pattern recognition, Las Vegas, USA, 27-30 2016.
  5. LeCun, Y. et al., 1995. Learning algorithms for classification: A comparison on handwritten digit recognition. Neural networks: the statistical mechanics perspective, pp. 261-276.
  6. Simonyan, K. & Zisserman, A., 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409, 1556, pp.1-14.
  7. Shora, M., Ghassemi, H. & Nowruzi, H., 2017. Using computational fluid dynamic and artificial neural networks to predict the performance and cavitation volume of a propeller under different geometrical and physical characteristics, Journal of Marine Engineering & Technology, 17(2), pp. 59-84.
  8. Szegedy, C. et al., 2015. Going deeper with convolutions. IEEE conference on computer vision and pattern recognition, Boston, USA, 7-12 June 2015.