MAINTENANCE AND REPAIR OF SHIP EQUIPMENT
Rubrics: TRANSPORT SYSTEMS
Authors:
1.
Maritime State University named after admiral G.I.Nevelskoy
2.
Maritime State University named after admiral G.I.Nevelskoy
Russian Federation
Russian Federation
Research supervisor:
Grickevich R. A.
Type:
Article
Pages:
from 99
to 104
Status:
Published
Received:
14.01.2025
Accepted:
21.02.2025
Published:
03.03.2025
Subject area:
629.5.06
Language:
Russian
Keywords:
maintenance, ship equipment repair, vibration diagnostics, thermography, ultrasonic testing, artificial intelligence, big data, robotics, MTBF, predictive models, digital twins, automation, shipping
Abstract (English):
This article explores contemporary approaches to the maintenance and repair of ship equipment, incorporating the use of artificial intelligence technologies, big data analytics, and robotics. An analysis of key diagnostic methods, such as vibration diagnostics, thermography, and ultrasonic testing, is conducted, alongside a description of automated systems for monitoring and fault prediction. Special emphasis is placed on optimizing maintenance processes through the analysis of Mean Time Between Failures (MTBF) data and the development of predictive models. The paper presents examples of the implementation of innovative solutions, including robotic devices and remote repair management systems, which enhance vessel reliability and reduce costs. Prospects for further technological advancements are discussed, including the creation of autonomous maintenance systems and digital twins of ships.
This article explores contemporary approaches to the maintenance and repair of ship equipment, incorporating the use of artificial intelligence technologies, big data analytics, and robotics. An analysis of key diagnostic methods, such as vibration diagnostics, thermography, and ultrasonic testing, is conducted, alongside a description of automated systems for monitoring and fault prediction. Special emphasis is placed on optimizing maintenance processes through the analysis of Mean Time Between Failures (MTBF) data and the development of predictive models. The paper presents examples of the implementation of innovative solutions, including robotic devices and remote repair management systems, which enhance vessel reliability and reduce costs. Prospects for further technological advancements are discussed, including the creation of autonomous maintenance systems and digital twins of ships.
Keywords:
maintenance, ship equipment repair, vibration diagnostics, thermography, ultrasonic testing, artificial intelligence, big data, robotics, MTBF, predictive models, digital twins, automation, shipping
maintenance, ship equipment repair, vibration diagnostics, thermography, ultrasonic testing, artificial intelligence, big data, robotics, MTBF, predictive models, digital twins, automation, shipping
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