CMS for Wind Turbines

For the last 10 years RSL has applied its innovative Machine Learning technology for diagnostic applications to wind turbines in the renewable energy sector. Our CMS system, the WT-HUMS, is suitable for wind turbines of all OEMs, including gearless wind turbines.

Main Features

  • the System's diagnostics, based on Learning Machines algorithms, determines accurate vibration thresholds for the wind turbine components of all manufacturers.
  • Real-time monitoring & diagnostics of all the wind turbine rotating components.
  • Monitoring the oscillations of the wind turbine's tower (optional)
  • Real-time alerts generation.
  • Continuous operation throughout all the operation modes of the wind turbine.
  • Control room station enables full visibility of wind farm condition with a graphical user-friendly interface.
  • Control room station enables optional remote-access viewing and operation.
  • Storage of the raw and processed data as well as alerts history for referencing and analysis.
    • Gearbox and gear meshing (including planetary gear), bearings, and shafts.
    • Main Shaft unbalance & misalignment
    • Main Bearings
    • Generator bearings
    • Turbine blades un-balance detection



  • Increase availability by shifting from scheduled-based to condition based maintenance. Early fault detection enables maintenance planning prior to breakage and helps optimize maintenance outages.
  • Increased revenues as a by-product of the increased availability and output generation.
  • Reduction of Maintenance cost  enabled by early fault detection which allows some of the up-tower maintenance to be performed prior to breakage thereby eliminating the un-planned need for a crane and its associated costs.
  • Decrease of spare parts inventories enabled by the early detection of a deterioration in the condition of a wind turbine part which allows the reduction of the levels of  in-stock spare parts "emergency level"


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