AI is commonly used in condition-based maintenance and remote diagnostics. Advanced algorithms analyse data from onboard sensors and predict equipment failures before they occur. In turn, this allows maintenance to be scheduled before the machinery malfunctions, prolonging equipment lifespan and ensuring crew safety. Data-driven condition-based maintenance can help to customise maintenance schedules, optimise resource utilisation and reduce operational disruptions.
In June 2024, LR, Nippon Yusen Kabushiki Kaisha (NYK Line) and MTI identified during their joint research that the adoption of data-driven condition-based maintenance (DCBM) can significantly impact vessel efficiency and reliability. The study showed that ships that deployed CBM processes saved millions of dollars across various services. For instance, employing CBM for lubrication services resulted in a US $1 million saving for vessels in service for 10 years.
A US $2 million saving for repairs and another 2 million for docking services were also achieved by vessels using CBM. For vessels that had been in service for 20 years, the OPEX for repair services dropped from US $10 million to 5 million, showing a substantial long-term financial benefit as a direct result of deploying CBM services.
Another example that highlights the importance of CBM is the application of Kaiko Systems’ vessel condition monitoring software across 18 F. Laeisz vessels. Kaiko Systems has an app-based tool that is used for ship inspections. It uses AI to structure and analyse data collected on board by the crew. This provides insights into the condition of the fleet and identifies potential risks in near real-time. The collected data is analysed using AI algorithms to identify patterns, predict maintenance needs, and detect potential issues before they escalate.
F. Laeisz was looking to improve its maintenance processes. The Kaiko system aims to boost collaboration between teams on land and onboard, ensuring continuous monitoring of the vessels from shore, compared with the previous process of carrying out two to three ship visits per year.
Harald Schlotfeldt, Technical Director at F. Laeisz said in a recent press release that they have high expectations for the new system. “The software must, of course, be state-of-the-art. It must be easy and quick to implement so that the changeover is as simple and convenient as possible for our teams on board and ashore. And it must help us to continuously optimise the condition of our fleet and thus prevent unpleasant surprises during maintenance procedures.”
Autonomous navigationÂ
AI is commonly used to help support decision-making when it comes to autonomous navigation.
Israeli startup Orca AI applies AI to improve situational awareness and enhance navigation safety. One of its offerings is a fully automated watchkeeper that processes multiple sources of visual information during navigation at sea. It mimics and supports human watchkeeping in real-time.
The company has worked with shipping lines MSC, NYK, Maersk, and Seaspan and has cited a 33% reduction in close encounters and a 40% reduction in crossing events across 15 million nautical miles thanks to its technology. The company has already secured significant funding to develop its technologies. In May 2024, it announced an additional $23 million, led by OCV Partners and Mizmaa Ventures, bringing its total to nearly $40 million.
To learn more about AI and its use in condition-based maintenance, read our thought leadership report, produced in collaboration with Lloyd’s Register titled, “Beyond the Horizon, Opportunities and Obstacles in the Maritime AI Boom” This report offers an in-depth analysis of the current state of operational AI in the shipping industry, shedding light on the latest trends, groundbreaking developments, and successful implementations. You can download a copy of the report here.Â
