An advanced machine vision-based method for abnormal detection of transverse vibrations in ship propulsion shafting

• Published in: Ocean engineering Vol. 314; p. 119724
• Main Authors: Zou, Yongjiu, Zhang, Kexin, Dong, Fangyang, Zhang, Peng, Cao, Lele, Luo, Si, Jiang, Xingjia, Du, Taili, Peng, Shitao, Zhang, Yuewen, Sun, Peiting, Xu, Minyi
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.12.2024
• Subjects: Abnormal detection; Intelligent ship; Machine vision; Propulsion shafting; Transverse vibration; Intelligent ship; Transverse vibration; Propulsion shafting; Machine vision; Abnormal detection
• DOI: 10.1016/j.oceaneng.2024.119724
• ISSN: 0029-8018

The working environment of ship propulsion shafting is harsh and the force condition is complex, which often produces all-directional vibration. Its working condition will directly affect the navigation performance of the ship. To overcome the limitations of complicated installation route, tedious maintenance process and high cost of traditional contact vibration sensors, an approach of transverse vibration identification model based on machine vision was proposed to realize multi-point vibration displacement sensing and anomaly analysis of shafting. The displacement information of the video signal is extracted by the displacement sensing strip labeling method, and the abnormal state of the ship propulsion shafting is analyzed by the dynamic kernel principal component analysis (DKPCA) algorithm. The experimental results show that the approach can accurately detect the continuous vibration displacement of shafting in the range of 180 r/min, and can work normally under two abnormal conditions: sudden external excitation and continuous uneven external excitation. In addition, this approach can quickly and accurately monitor the motion state of shafting, and realize the perception and recognition of abnormal vibration state of shafting. The research and application of this approach in ship shafting vibration monitoring is of great significance to the development of unmanned and intelligent ships. •An advanced machine vision-based vibration displacement monitoring method for ship shafting is proposed.•Pixel gray gravity center method is used to extract the center coordinates of fringe pattern on shafting.•Compared with the traditional sensor, the proposed method avoids multiple installations, debugging and calibration.