Current-based online bearing fault diagnosis for direct-drive wind turbines via spectrum analysis and impulse detection

Online fault diagnosis is an effective means to improve wind turbine reliability and performance and reduce wind turbine operating and maintenance costs. Current-based fault diagnosis techniques have received more and more attention in academia and industry due to their nonintrusive character and economic advantages. This paper presents an algorithm based on power spectral density (PSD) analysis for bearing fault signature extraction of direct-drive wind turbines by only using stator current measurements. An impulse detection method is then applied to screen out the excitations in the PSD spectrum, where the excitations at the characteristic frequencies of the bearing fault are extracted as the fault signature. A median filter-based method is then designed to evaluate the physical condition of the wind turbine based on the extracted fault signature to determine whether maintenance is required. Experimental results are provided to demonstrate the effectiveness of the proposed method for bearing faults diagnosis of a direct-drive wind turbine operating at variable-speed conditions.