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http://dx.doi.org/10.3744/SNAK.2022.59.2.125

Fault Detection of Propeller of an Overactuated Unmanned Surface Vehicle based on Convolutional Neural Network  

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)
Publication Information
Journal of the Society of Naval Architects of Korea / v.59, no.2, 2022 , pp. 125-133 More about this Journal
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
Unmanned surface vehicle; Fault detection; Convolutional neural network; ROS; Overacturated; Wavelet transform;
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Times Cited By KSCI : 1  (Citation Analysis)
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