Generator bearing fault diagnosis for wind turbine via empirical wavelet transform using measured vibration signals


Condition monitoring system for the wind turbine drivetrain in Nan’ao island wind farm is designed and developed.

Weak fault and compound fault diagnosis method for generator bearing of wind turbine is proposed based on empirical wavelet transform.

Experimental validation and engineering application is carried out to demonstrate the feasibility of the proposed generator bearing fault diagnosis method.


The implementation of condition monitoring and fault diagnosis system (CMFDS) on wind turbine is significant to lower the unscheduled breakdown. Generator is one of the most important components in wind turbine, and generator bearing fault identification always draws lots of attention. However, non-stationary vibration signal of weak fault and compound fault with a large amount of background noise makes this task challenging in many cases. So, effective signal processing method is essential in the accurate diagnosis step of CMFDS. As a novel signal processing method, empirical Wavelet Transform (EWT) is used to extract inherent modulation information by decomposing signal into mono-components under an orthogonal basis, which is seen as a powerful tool for mechanical fault diagnosis. Moreover, in order to avoid the inaccurate identification the internal modes caused by the heavy noise, wavelet spatial neighboring coefficient denoising with data-driven threshold is applied to increase Signal to Noise Ratio (SNR) before EWT. The effectiveness of the proposed technique on weak fault and compound fault diagnosis is first validated by two experimental cases. Finally, the proposed method has been applied to identify fault feature of generator bearing on wind turbine in wind farm successfully.


  • Wind turbine;
  • Generator bearing;
  • Weak fault and compound fault diagnosis;
  • Empirical wavelet transform;
  • Spatial neighboring coefficient

Be the first to comment

Leave a Reply

Your email address will not be published.