Fault diagnosis of wind turbine rolling bearing based on wavelet and Hilbert transforms

Rolling bearing is not only one of vulnerable components of wind turbine but also one of the most prone to failure components, so fault diagnosis and monitoring of the rolling bearing is the focus. Vibrational analysis is widely used for analysis of bearings. However, extraction of fault signatures from practical signals is always a great challenge. This paper proposes a new method for identifying incipient failures based on monitoring certain statistical parameters and a combination of the Hilbert and wavelet transforms. Then fault diagnosis system of wind turbine rolling bearing has been developed in LabVIEW 8.5 professional Edition. Experimental results have proved that the developed system can efficiently identify rolling bearing fault.