Project diagram Copyright: © CWD  

Project for the holistic optimization of wind turbine generator utilisation through innovative sensor systems "ProNOWIS"

Aim of "ProNOWIS"

The aim of the project is the upgrading of sensor components in order to develop a practical condition monitoring system for the early detection of occurrence and propagation of White Etching Cracks (WEC) of wind turbines drivetrain. There are three main working packages:

  • Big Data approach on WEC
  • Material and sensor tests
  • Turbine tests on 4 MW test bench

White Etching Cracks (WEC)

The White Etching Cracks (WEC) is a failure mode that appears on bearings with rolling elements. It leads to sudden failure of the bearing at very early stages of the calculated lifetime of the product (typical at 10% to 20% of the service life). Besides the spares, this failure causes expensive maintenance and operation costs due to unexpected repair tasks and long down times on the wind turbines. Prior scientific and industry research projects confirmed the influence of materials and lubrication on White Etching Cracks (WEC) emergence, together with additional electric voltage on the mechanical parts. It was also demonstrated that the processes related to White Etching Cracks generation could be depicted with help of innovative detection techniques like Barkhausen Noise and HFIM (high-frequency impulse measurement).


All the above suggest, that a better facility availability can be achieved through selective surveillance of the relevant factors. Nevertheless, The White Etching Cracks (WEC) remain to the date undetectable by condition monitoring systems. The large challenge is now to find the related parameters for the formation of White Etching Cracks with the help of latest data analysis techniques. The basis for this is an extensive screening of historic records from field facilities, data bases created on previous investigations about White Etching Cracks (WEC) and ongoing activities. The variables describing the damage patterns serve then as input variables for the design and calibration of new sensor concepts. The sensor components are then installed and trained on scaled test benches at the IPAT/Erlangen and a 4 MW real-size nacelle test bench at CWD. This iterative process eventually results on a functioning condition monitoring system able to assess WEC damage pattern. As part of the sensor-based solution approach there will be a verification of robust and interacting lubricants, which proved to be especially WEC-resistant on already undertaken bench tests. For this purpose specific sensors which allow the online surveillance of the oil status will be pursued.


Intelligent and integrated sensor systems are a key contribution for the digitalization of the powertrain. This one will eventually lead to autonomic systems, which have higher operational availability and lower maintenance costs (and therefore, greater profitability). Under this vision, there is a huge improvement potential in wind parks that can be exploited.


01.10.2016 - 30.9.2019

Associated companies:

Fuchs Schmierstoffe GmbH Hydac Electronic GmbH Qass (Qualität Automation Systeme Software) GmbH Qiagen Lake Constance GmbH Schaeffler Technologies AG & Co. KG

Project funded by:

Federal Ministry for Economic Affairs and Energy

Projekt promoted by:

Projektträger Jülich