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

The Society of Naval Architects of Korea (대한조선학회)
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Fault Detection of Propeller of an Overactuated Unmanned Surface Vehicle based on Convolutional Neural Network

합성곱신경망을 활용한 과구동기 시스템을 가지는 소형 무인선의 추진기 고장 감지

  • Baek, Seung-dae (Department of Naval Architecture and Ocean System Engineering, Korea Maritime and Ocean University) ;
  • Woo, Joo-hyun (Major of Naval Architecture and Ocean System Engineering, Korea Maritime and Ocean University)
  • 백승대 (한국해양대학교 조선해양시스템공학과) ;
  • 우주현 (한국해양대학교 조선.해양개발공학부)
  • Received : 2022.01.03
  • Accepted : 2022.02.21
  • Published : 2022.04.20
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Abstract

This paper proposes a fault detection method for a Unmanned Surface Vehicle (USV) with overactuated system. Current status information for fault detection is expressed as a scalogram image. The scalogram image is obtained by wavelet-transforming the USV's control input and sensor information. The fault detection scheme is based on Convolutional Neural Network (CNN) algorithm. The previously generated scalogram data was transferred learning to GoogLeNet algorithm. The data are generated as scalogram images in real time, and fault is detected through a learning model. The result of fault detection is very robust and highly accurate.

Keywords

Acknowledgement

이 논문은 2021년도 정부(미래창조과학부)의 재원으로 한국연구재단 생애 첫 연구 사업의 지원을 받아 수행된 연구임(NRF-2021R1G1A1095671)

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