Electrical & Computer Engineering, Department of


Date of this Version



D. Lu, X. Gong, and W. Qiao, “Current-based diagnosis for gear tooth breaks in wind turbine gearboxes,” in Proceedings of IEEE Energy Conversion Congress and Exposition 2012 (ECCE 2012), Raleigh, NC, USA, Sept. 15-20, 2012, pp. 3780-3786.


(First Prize of the 2012 Best Paper Awards of the IEEE IAS Renewable and Sustainable Energy Conversion Systems Committee).


Gearbox faults constitute a significant portion of all faults and downtime in wind turbines (WTs). Current-based gearbox fault diagnosis has significant advantages over traditional vibration-based techniques in terms of cost, implementation, and reliability. This paper derives a mathematical model for a WT drive train consisting of a twostage gearbox and a permanent magnet (PM) generator, from which the characteristic frequencies of gear tooth breaks in generator stator current frequency spectra are clearly identified. A adaptive signal resampling algorithm is proposed to convert the variable fault characteristic frequencies to constant values for WTs running at variable speeds. A fault detector is proposed for diagnosis of gear tooth breaks using statistical analysis on the fault signatures extracted from the stator current spectra. Experimental results on a real gearbox are provided to show the effectiveness of the proposed model and method for diagnosis of gear tooth breaks.