해상크레인 윈치 감속기의 기계학습기반 실시간 상태모니터링 시스템 개발

• Published in: 한국CDE학회 논문집 Vol. 25; no. 4; pp. 445 - 454
• Main Authors: 황세윤(Se-Yun Hwang), 이장현(Jang-Hyun Lee), 김광식(Kwang-Sik Kim), 오재원(Jae-Won Oh), 민천홍(Cheon-Hong Min)
• Format: Journal Article
• Language: Korean
• Published: (사)한국CDE학회 01.12.2020; 한국CDE학회
• Subjects: 기계공학; Condition monitoring; Floating Crane; Naive Bayes classifier; Prognostics and health management; Real-time monitoring
• ISSN: 2508-4003; 2508-402X
• DOI: 10.7315/CDE.2020.445

This study introduces development examples of monitoring system for winch equipment, the main equipment of floating cranes. The detail process was introduced to develop a system that can acquire sensor data in real time, monitor operating conditions and fault diagnosis. The proposed monitoring system is designed for winch equipment, which is a key equipment of the offshore crane. The system was developed for bearing part, which frequently causes failures in the winch equipment. In addition, we would like to introduce a relatively low-cost H/W configuration to facilitate application in small and medium-sized industries. The monitoring methods have been implemented by applying the method of Naïve Bayes classification based on the method of supervised learning. KCI Citation Count: 0