Content area
Modern ships are complex technical units composed of various systems and subsystems, including the ship management system, safety and protective systems for the ship and crew, propulsion system, hull structure, power supply and distribution system, cargo handling system, and accommodation systems for crew and passengers. All these systems are interconnected and collectively contribute to the ship's purpose, primarily achieved through comprehensive maintenance. Increasingly rigorous international regulations regarding ship and crew safety, as well as environmental protection, encourage shipowners to emphasize regular, proactive, and predictive maintenance of ship systems to ensure uninterrupted operations without delays. Advancements in modern technology have transformed approaches to ship maintenance by leveraging developments in the ICT sector, including analytical methods and AI models like machine learning and deep learning, which have revolutionized maintenance practices. This paper, in addition to exploring conventional maintenance methods, investigates key components of predictive maintenance, discusses challenges such as transparency and ethical implications, and analyzes the latest trends in AI-based predictive maintenance-from sensors and data collection to data processing algorithms and decision-making modules.
Details
Data processing;
Deep learning;
International regulations;
Communication;
Data analysis;
Propulsion systems;
Machine learning;
Data collection;
Preventive maintenance;
Shipping;
Environmental protection;
Artificial intelligence;
Maintenance costs;
Classification;
Lubricants & lubrication;
Algorithms;
Power supply;
Ship hulls;
Subsystems;
Bridges;
Crew;
Maintenance;
Cargo handling;
Decision making;
Passengers;
Predictive maintenance;
Safety management
1 Adriatic Croatia International Club, ACI Club d.d., Rudolfa Strohala 2, 51000 Rijeka, Croatia
2 University of Rijeka, Faculty of Maritime Studies, Studentska 2, 51000 Rijeka, Croatia