Explainable AI systems improve maritime equipment failure prediction
Explainable Failure Prediction and Prevention in Maritime
Artificial Intelligence
Summary
Maritime systems like ships and offshore platforms work in tough and changing conditions where equipment can suddenly break down, risking safety and operations. The authors review how artificial intelligence techniques can help predict these failures before they happen and suggest ways to fix them. They focus on making sure these AI decisions can be explained to humans, which is important for trust and safety. The chapter also discusses challenges like limited data, the need for reliable models, and deploying these tools in real maritime settings.
What this means in practice
- •For maritime operators: Integrate explainable AI tools to anticipate and prevent equipment failures enhancing safety and operation continuity.
- •For industrial maintenance teams: Adopt advanced predictive maintenance frameworks with explainability to better plan repairs and reduce unexpected downtime.
A survey. It maps existing work.
Authors
Dionisis Kalogeropoulos, Georgia Sovatzidi, Panagiotis G. Kalozoumis, Dimitris K. Iakovidis
Abstract
Maritime systems operate in highly dynamic environments where unexpected equipment failures can compromise safety, reliability, and operational efficiency. Recent advances in artificial intelligence (AI), machine learning, digital twins, and predictive maintenance enable proactive failure prediction and prevention. However, ensuring trustworthy and explainable decision-making remains a major challenge in safety-critical maritime applications. This chapter reviews key AI technologies required for explainable failure prediction and prevention in maritime systems and presents a conceptual architecture capable of supporting autonomous or human-in-the-loop corrective actions. This architecture integrates data acquisition, time-series forecasting, anomaly detection, risk assessment, decision-making, and explainable AI into a closed-loop framework. With reference to the architectural components, a review and discussion of relevant maritime studies is performed, outlining their methods, advantages, and limitations. Furthermore, it highlights current challenges, including uncertainty and robustness, model generalization, explainability, limited availability of maritime datasets, and operational deployment, and identifies future research directions toward trustworthy AI-assisted maritime decision-making.