ESR 5 2017-05-10T16:27:13+00:00


Development of Wind Turbine Fault Detection Algorithms

The research will focus on the analysis of standard wind turbine SCADA data as well as addition signals including vibration, high frequency monitoring of electrical power output, oil analysis and blade strain data (if available) to determine the principal drivers of damage and failure modes building on the results of past UK and EU projects. There will also be an opportunity to undertake small scale experiments using test rigs at Loughborough University and at partners in the UK and abroad (e.g. Supergen Wind partners and contacts in Korea and China).

The data gathered and analysed will be used to develop condition monitoring algorithms based on damage models, trending, FFTs, wavelets and artificial intelligence techniques (e.g. neural networks). There will be an emphasis on estimating expected reliability to target maintenance in large fleets of machines as it is acknowledged that accurate prognosis is difficult.

The projects objectives are:

  • To analyse wind turbine SCADA and additional condition monitoring data in order to determine failure signatures.

  • To relate these failures to the root causes.

  • To develop algorithms to detect the likelihood of future failures through the use of models such as physical damage accumulation equations, trending, FFTs, wavelets, etc.

  • To carry out measurements on small-scale machines or test rigs to help inform the algorithm development..

Planned Secondments


Tentative schedule: March – July 2016


Tentative schedule: March – July 2017



  • Ibrahim, RK, Tautz-Weinert, J, Watson, SJ (2016) Neural Networks for Wind Turbine Fault Detection via Current Signature Analysis. WindEurope Summit 2016, Hamburg. Full text:

  • Tautz-Weinert, J and Watson, SJ (2017) Condition monitoring of wind turbine drive trains by normal behaviour modelling of temperatures. In Abel, Brecher, De Doncker, Hameyer, Jacobs, Monti, Schröder (ed) Conference for Wind Power Drives (CWD 2017), Aachen, pp.359-372, ISBN: 9783743134560. Full text:


  • Weinert, J and Watson, SJ (2016) Condition monitoring by neural network modelling of drive train temperature, 12th EAWE PhD Seminar on Wind Energy in Europe. Full text:

  • Weinert, J and Watson, SJ (2015) Wind Turbine Fault Detection by Normal Behaviour Modelling, Midlands Energy Consortium Postgraduate Student Conference. Full text:

  • L. Colone, M. Reder, J. Tautz-Weinert, J.J. Melero, A. Natarajana, S.J. Watson (2017). Optimisation of Data Acquisition in Wind Turbines with Data-Driven Conversion Functions for Sensor Measurements. IEERA DEEPWIND’2017, 14TH DEEP SEA OFFSHORE WIND R&D CONFERENCE, Trondheim, Norway. Download Poster


Jannis Tautz-Weinert
MSc Energy Engineering


Simon Watson

Country: UK
Host Institution: LBORO