Turbine operations and degradation monitor


  • Remote, globally installed fleet
  • Turbines degrade in between services and cleaning


  • Monitoring and diagnosis of turbine degradation

Our Contribution

  • Detection of operation patterns in hundreds of years of turbine operation data
  • Development of equivalent operation hours (EOH) algorithms based on real start stop, part load and critical operation
  • Investigation of various key performance indicators for efficient degradation detection, modelling and trending
  • Automation of operations and degradation modelling with machine learning based on historic fleet data