This paper explores the application of artificial intelligence, machine learning, digital twins, and predictive maintenance to forecast and prevent equipment failures in maritime systems. It addresses the critical challenge of ensuring trustworthy and explainable decision-making in these safety-critical environments. The authors propose a conceptual architecture that integrates various AI technologies for a closed-loop framework, while also discussing current limitations and future research directions for AI-assisted maritime operations. AI
IMPACT This research could lead to more reliable and safer maritime operations through advanced failure prediction and prevention systems.
RANK_REASON The item is an academic paper published on arXiv discussing AI applications. [lever_c_demoted from research: ic=1 ai=1.0]
- alphaXiv
- arXiv
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- CORE Recommender
- DagsHub
- digital twin
- explainable AI
- Gotit.pub
- Hugging Face
- Influence Flower
- machine learning
- predictive maintenance
- ScienceCast
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