Predictive Maintenance for Sustainable Marine Operations Using Machine Learning and Deep Learning
Künye
Kıyak, E. (2025). Predictive Maintenance for Sustainable Marine Operations Using Machine Learning and Deep Learning. In The Role of Exergy and Energy in Sustainability (pp. 433-447). Cham: Springer Nature Switzerland.Özet
The role of the maritime industry is essential in global trade and economic growth, but it faces challenges, including maintenance and environmental impact. With Industry 4.0 introducing advanced technologies like Machine Learning (ML) and Deep Learning (DL) that provide innovative solutions for predictive maintenance, this section delves into how the use of ML and DL can lead to a complete overhaul of traditional maintenance approaches. It discusses operational efficiency through prediction-based systems due to their effective implementation, which involves adoption from the theories as explained using real-world examples, thus stopping reactive practices that only manage equipment when it breaks down. We also review data considerations—as well as implementation strategies—to equip stakeholders with practical knowledge that will allow them to easily navigate through this complicated field of technology in the future. In conclusion, the chapter presents a three-dimensional view of how tapping artificial intelligence can help slash maintenance costs—minimizing equipment downtime—greasing the wheels for more eco-friendly marine operations.

















