The existing methods of ship inspection and maintenance rely on manual operation and empirical judgment, which have problems of low efficiency and difficulty in precise prediction. This article adopts an intelligent ship inspection and maintenance system based on deep learning technology, aiming to improve detection precision and maintenance predictability, thereby optimizing ship operation efficiency. Firstly, the system utilizes the image identification technology and convolutional neural network (CNN) models to automatically identify defects such as cracks and corrosion on the surface of the ship bodies. Secondly, by analyzing sensor data through long short-term memory (LSTM) networks, potential equipment failures are predicted, and maintenance schedules can be optimized, thereby reducing unexpected shutdowns. Meanwhile, multi-source data fusion and ensemble learning strategies are adopted to further enhance the reliability and prediction precision of the model. The results show that the system performs significantly in multiple key areas: crack detection precision of 91.5% and recall rate of 94.0%; corrosion detection precision of 90.8% and recall rate of 89.5%. The research shows that the application of deep learning technology provides an innovative method for intelligent inspection and maintenance of ships, significantly improving operational efficiency and management effectiveness.
Application and Optimization in Ship Inspection and Intelligent Maintenance
Smart Innovation, Systems and Technologies
International Conference on 3D Imaging Technologies ; 2024 ; Sanya, China November 22, 2024 - November 24, 2024
2025-08-13
14 pages
Article/Chapter (Book)
Electronic Resource
English
Ship intelligent maintenance equipment and maintenance process
European Patent Office | 2020
|Inspection, repair and maintenance of ship structures
TIBKAT | 2001
|Intelligent pipeline inspection and maintenance robot
Tema Archive | 1985
|