Integral structural monitoring system for floating offshore wind assets.

The SIMEWind monitoring solution can be integrated into barge, semi-submersible, spar or TLP type substructures; concrete, steel or hybrid.

SIMEWind integrates in a digital twin:

  • Sensor data for the measurement of structural variables.
  • Data of the meteorological and meteoceanic conditions to which the asset is subjected.
  • Structural and hydrodynamic physical-virtual models.

  • Machine learning algorithms.

SIMEWind is a modular and integral system composed of:

Interface Tower-Floater

Mooring Lines

Dynamic Cable

Floater Structure + Heavy Plate

SIMEWind First Unit

It is the tool that facilitates the acquisition of knowledge about the structural and hydrodynamic behavior of the platform based on the acquisition, interpretation and analysis of real operational data.

Accurately measures and quantifies

  • The dynamics and motion of the floating substructure and relative deformations.

  • The distribution of aerodynamic and meteoceanic loads – forces and moments – at critical interfaces, tower, floating substructure, heave plate or dynamic cable.

  • Real stresses at interfaces or critical points of the different modules: Substructure welds, tower joints, hub, heave plate welds, lugs and mooring lines, dynamic cable, etc..
 Analyze and relate
  • External loads and their effect on the structural and hydrodynamic response of all elements and modules of the wind asset.


  • Structural and hydrodynamic virtual models are key tools to provide physical interpretation to the acquired data. SIMEWind correlates the models with real data to make them representative to be used as validation tools in the design and development phase.

SIMEWind Serial


As a complete structural health monitoring system


It analyzes and diagnoses

  • How is the real-time structural health of components and structure.
  • Fatigue damage in critical bolted or welded joints.
  • Fatigue damage at tower and floating substructure interfaces, mooring lines, heave plate, dynamic cable.


  • Structural risk through early detection of working anomalies.

Facilitates predictive maintenance

  • Real data from sensors are used to feed continuosly the digital twin, and also to apply machine learning algorithms, and both provide structural health predictive capability.

  • SIMEWind predicts the hydrodynamic behavior of mooring lines, dynamic cable, heave plate and substructures to anticipate structural health diagnosis.