Version 1
: Received: 11 September 2024 / Approved: 11 September 2024 / Online: 12 September 2024 (03:26:44 CEST)
How to cite:
Simion, D.; Postolache, F.; Fleacă, B.; Fleacă, E. AI-Driven Predictive Maintenance in Modern Maritime Transport. Enhancing Operational Efficiency and Reliability. Preprints2024, 2024090930. https://doi.org/10.20944/preprints202409.0930.v1
Simion, D.; Postolache, F.; Fleacă, B.; Fleacă, E. AI-Driven Predictive Maintenance in Modern Maritime Transport. Enhancing Operational Efficiency and Reliability. Preprints 2024, 2024090930. https://doi.org/10.20944/preprints202409.0930.v1
Simion, D.; Postolache, F.; Fleacă, B.; Fleacă, E. AI-Driven Predictive Maintenance in Modern Maritime Transport. Enhancing Operational Efficiency and Reliability. Preprints2024, 2024090930. https://doi.org/10.20944/preprints202409.0930.v1
APA Style
Simion, D., Postolache, F., Fleacă, B., & Fleacă, E. (2024). AI-Driven Predictive Maintenance in Modern Maritime Transport. Enhancing Operational Efficiency and Reliability. Preprints. https://doi.org/10.20944/preprints202409.0930.v1
Chicago/Turabian Style
Simion, D., Bogdan Fleacă and Elena Fleacă. 2024 "AI-Driven Predictive Maintenance in Modern Maritime Transport. Enhancing Operational Efficiency and Reliability" Preprints. https://doi.org/10.20944/preprints202409.0930.v1
Abstract
Maritime transport has adapted to recent political and economic shifts by addressing stringent pollution reduction requirements, redrawing transport routes for safety, reducing onboard technical incidents, managing data security risks and transitioning to autonomous vessels. The integration of AI-based technologies for fault diagnosis and decision-making, coupled with proper personnel training, has become essential for optimal ship operation, with AI-assisted predictive maintenance emerging as the key strategy to ensure onboard systems function within desired parameters and enhance equipment availability. This study proposes a machine-learning algorithm for evaluating onboard systems performance by analyzing sensor data and mapping it to fault patterns to estimate functional states, validated through tests on a seawater cooling system from an oil tanker, demonstrating operational efficiency and reliability. The immediate advantage is reduced time for fault diagnosis by rapidly assessing the system using operational data, the algorithm enabling real-time monitoring and fault data management to minimize efforts as part of predictive maintenance assisted by machine learning tools.
Keywords
artificial intelligence; machine learning; KNN; big data analysis; maritime fault diagnosis, FMECA, ship maintenance
Subject
Engineering, Marine Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
