합성곱신경망을 활용한 과구동기 시스템을 가지는 소형 무인선의 추진기 고장 감지

• Published in: 大韓造船學會 論文集 Vol. 59; no. 2; pp. 125 - 133
• Main Authors: 백승대(Seung-dae Baek), 우주현(Joo-hyun Woo)
• Format: Journal Article
• Language: Korean
• Published: 대한조선학회 2022
• Subjects: 조선공학; Unmanned surface vehicle(무인 수상 선박); Wavelet transform(웨이블렛 변환); Convolutional neural network(합성곱신경망); Fault detection(고장 감지); ROS; Overacturated(과구동기)
• ISSN: 1225-1143; 2287-7355
• DOI: 10.3744/SNAK.2022.59.2.125

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.