MoorIA Project: Artificial Intelligence for mooring monitoring in floating offshore wind

MoorIA is an innovative research project applying artificial intelligence (AI) to revolutionize maintenance in floating offshore wind platforms. Developed under the HAZITEK program, the project is led by INALIA Innovación y Desarrollo in partnership with Saitec Offshore Technologies.

Floating offshore wind energy is a key driver of the global energy transition and decarbonization. However, its main current challenge lies in reducing operation and maintenance (O&M) costs.
The success of these platforms relies heavily on their mooring systems (chains, synthetic or steel cables), which are responsible for keeping the structure safely in position against harsh marine conditions.

The reliability of these moorings is critical not only for safety but also for the economic viability of the wind farm. In fact, insurance companies and financial institutions require rigorous proof of these systems’ robustness before backing any project.

Currently, mooring monitoring faces significant cost and durability limitations, often restricting its use to short-term prototypes. The industry urgently needs scalable, reliable, and low-maintenance solutions.

The MoorIA solution: SHM and deep learning

MoorIA was created to address this challenge. The project aims to develop new Structural Health Monitoring (SHM) tools powered by predictive artificial intelligence models.

Unlike traditional methods that rely on direct measurements, which are complex and expensive to install on operating platforms, MoorIA uses an innovative approach: estimating mooring loads through indirect measurements. To achieve this, the system integrates:

• Operational data: analyzing the platform’s movement and position.
• Proven maritime technology: utilizing MRU (Motion Reference Unit) and GPS + RTK sensors, known for their high precision in the naval sector.
• Deep learning: advanced algorithms that translate these indirect data points into vital information about the condition of the mooring lines.

Project development phases

To ensure the success and reliability of this new monitoring system, MoorIA’s methodology is divided into three key stages:

1. AI algorithm training and validation: Using the DemoSATH floating platform to record real load data under various operating conditions.

1. Direct measurement system design: Creating a device that can be installed on operational platforms to overcome current technological barriers and calibrate the system.
2. Predictive model development: Continuous AI research to estimate load time series and detect structural anomalies through comprehensive indirect data analysis.

Key benefits of predictive maintenance

Implementing artificial intelligence in mooring system monitoring will mark a turning point in the profitability of offshore wind farms. By redefining the parameters of SHM, MoorIA enables a transition toward smarter, resource-efficient monitoring.

This project has received funding from the budget of the Department of Industry, Energy Transition, and Sustainability of the Basque Government, as well as the European Regional Development Fund (ERDF).

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