Field of work

Currently, Dr. Mahdi Ghane is an Associate professor at Department of Computer Science, Electrical Engineering, and Mathematical Sciences at the Western Norway University of Applied Sciences (HVL). He is also associated as a lecturer and researcher with Department of Marine Technology, NTNU. He is lecturing on the topic of Stochastic Methods Applied in Nonlinear Analysis of Marine Structures (PhD-level) as well as co-supervising a Ph.D. student on the topic of intelligent fault diagnosis of floating wind turbine drive train.  


His main research area is wind energy, fault diagnosis and fault tolerant control, reliability and optimization. He had direct collaboration with several industries such as Equinor, Yinson, Bane Nor, Aker Solution, Kverner and ABS. He is now working on an NFR project (InteDiag-WTCP) related to Centre for Floating Structures for the Next Generation Ocean Industries (SFI BLUES), Norwegian Research Centre on Wind Energy (FME NorthWind).



  • 2018 Ph.D. Marine Cybernetics Technology, NTNU-MIT-Statoil PhD Fellow, Centre for Autonomous Marine Operations and Systems, NTNU.
  • 2009 M.Sc. Mechatronics Engineering, K.N.Toosi University of Technology, Tehran (ranked 1st).
  • 2007 B.Sc. Electrical Engineering, Ferdowsi University of Mashhad (top student).


Awards and Honors:

  • 2016 Best paper award, the science of making torque from wind (Torque) conference, Munich, Germany.
  • 2013 Statoil-NTNU-MIT PhD Fellowship (Hywind Scotland Project)



  • 2016 Visiting Researcher, Technical University of Denmark, Department of Electrical Eng, Denmark.
  • 2014 Visiting Researcher, University of Melbourne, Department of Electrical Eng, Melbourne, Australia.


Memberships of academic societies:

  • 2017-2021 Member ASME
  • 2017-2021 Member IEEE
  • 2016-2021 Tekna


Courses taught


  • ELE 306 Robotics
  • ELE 302 Control Technology 2
  • ING104 Introduction to engineering practice and working methods


  • MR8503 - Stochastic Methods Applied in Nonlinear Analysis of Marine Structures (PhD-level)
Research areas
  • Renewable Energy (Wind Turbine Modeling and Control)
  • Signal Processing (Esp. Statistical and Stochastic Data Analysis)
  • Fault Detection and Fault Tolerant Control
  • Robotics (Modeling and Control)
  • System Modeling and Optimization (Discrete event modeling)
  • Artificial Intelligence and data mining


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