Predictive Maintenance for Sustainable Maritime Transportation and Offshore Wind Energy

Maritime transportation and offshore renewable energy systems form the backbone of sustainable ocean economies. As these infrastructures scale in complexity and operate in increasingly sensitive marine environments, there is growing demand for innovative solutions that enhance operational reliability while delivering regenerative and nature-positive outcomes for coastal and ocean ecosystems.

Predictive maintenance (PdM), enabled by advances in artificial intelligence, digital twins, IoT sensing, robotics, and big-data analytics, has emerged as a powerful tool to transition from reactive or schedule-based maintenance to intelligent, condition-based care. Beyond improving efficiency, PdM can reduce lifecycle emissions, minimize unnecessary maintenance vessel trips, extend asset longevity, and significantly decrease ecological disturbance associated with traditional offshore operations.

In maritime transportation, PdM contributes to safer and cleaner vessel operations, supports decarbonization goals, and optimizes fleet performance with fewer disruptions. In offshore wind farms, intelligent maintenance planning plays a key role in ensuring turbine reliability, reducing human exposure to hazardous environments, and lowering environmental impact across the asset lifecycle. However, major challenges remain, including limited data accessibility, model trustworthiness, interoperability, cybersecurity risks, and the need for integration with environmental governance and regulatory frameworks.

This Research Topic seeks interdisciplinary and solution-oriented contributions exploring how predictive maintenance can support the transition toward regenerative, resilient, and ecologically aligned ocean and coastal systems. We particularly welcome studies that integrate engineering, AI, environmental science, socio-economic analysis, and policy perspectives to demonstrate how PdM:
• reduces ecological disturbance and habitat impacts,
• contributes to blue natural capital and socio-ecological co-benefits,
• supports decarbonization and emissions reduction,
• enhances system resilience under climate stress,
• improves lifecycle sustainability of offshore and maritime assets.

Topics of interest include, but are not limited to:
• AI, machine learning, deep learning, and digital twins for predictive maintenance in ships and offshore wind farms
• Sensor-based monitoring and data-driven diagnostics for marine and offshore assets.
• Reliability, risk assessment, and failure prediction models in maritime and wind energy systems.
• Integration of predictive maintenance with decarbonization and sustainability strategies
• Cybersecurity and data governance challenges in predictive maintenance systems.
• Comparative studies of PdM in maritime transport vs. offshore renewable energy.
• Policy, regulation, and standards supporting predictive maintenance adoption.
• Case studies and pilot projects demonstrating real-world PdM applications.

We particularly welcome contributions that link technical innovation with broader implications for sustainability, resilience, and global energy transition. Manuscripts focusing solely on engineering details without system-level or policy relevance will not be considered.

Maritime and offshore renewable energy infrastructures are essential to the resilience of both industrialized and developing economies. We encourage submissions from diverse geographic regions, including emerging economies, Arctic/remote locations, and areas where PdM can yield significant socio-ecological benefits. Case studies addressing unique operational or environmental challenges are particularly welcomed.

This Research Topic will accept the following article types:
• Original Research
• Systematic Reviews and Mini Reviews
• Policy Papers and Policy Briefs
• Case Study Reports
• Methods and Data Reports
• Hypothesis and Theory
• Opinion and Perspective
• Technology and Code

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Policy Brief
• Review
• Systematic Review
• Technology and Code

Keywords: Predictive Maintenance, Marine Conservation, Ecosystem Sustainability, Offshore Renewable Energy, Artificial Intelligence

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.