합성곱신경망을 활용한 과구동기 시스템을 가지는 소형 무인선의 추진기 고장 감지
Fault Detection of Propeller of an Overactuated Unmanned Surface Vehicle based on Convolutional Neural Network
0
조회
0
다운로드
1
인용
0
참고문헌
키워드
초록
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.
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.
참고문헌 (11)
- M. Hadi Amoozgar, "Experimental Test of a Two-Stage Kalman Filter for Actuator Fault Detection and Diagnosis of an Unmanned Quadrotor Helicopter", Journal of Intelligent & Robotic Systems, 70(1-4), pp.107~117 (2012), Springer Science and Business Media LLC[DOI]
- Bingham, B., "Toward maritime robotic simulation in gazebo", OCEANS 2019MTS, 2019, ~
- Seongpil Cho, "Fault detection and diagnosis of a blade pitch system in a floating wind turbine based on Kalman filters and artificial neural networks", Renewable Energy, 169, pp.1~13 (2021), Elsevier BV[DOI]
- Fréchot, J., "Realistic simulation of ocean surface using wave spectra", Institute for Systems and Technologies of Information, Control and Communication. Proceedings of the first international conference on computer graphics theory and applications (GRAPP 2006), pp.76~83 (2006)
- S. Jayalakshmy, "Scalogram based prediction model for respiratory disorders using optimized convolutional neural networks", Artificial Intelligence in Medicine, 103, pp.101809~ (2020), Elsevier BV[DOI]
- Kim, B. M., "Trend of image classification technology based on deep learning", Korea Institute Of Communication Sciences, 35(12), pp.7~14 (2018)
- Szegedy, C., "Going deeper with convolutions", Proceedings of the IEEE conference on computer vision and pattern recognition, pp.1~9 (2015)
- Saman Taheri, "Fault detection diagnostic for HVAC systems via deep learning algorithms", Energy and Buildings, 250, pp.111275~ (2021), Elsevier BV[DOI]
- Tessendorf, J., "Simulating ocean water", Simulating nature:realistic and interactive techniques, 1(2), pp.5~ (2001), SIGGRAPH
- 윤석민, "다수의 추진기를 지닌 ROV의 추력배분을 통한 정지 상태에서의선수각 및 수심 제어 구현", 한국해양공학회지, 32(1), pp.68~75 (2018), 한국해양공학회
- Ming-jun Zhang, "Thruster fault diagnosis in autonomous underwater vehicle based on grey qualitative simulation", Ocean Engineering, 105, pp.247~255 (2015), Elsevier BV[DOI]