Compound faults diagnosis method for wind turbine mainshaft bearing with Teager and second-order stochastic resonance

Abstract

Low rotational speed and heavy load lead to the weak vibration energy and complexity of signals captured on wind turbine mainshaft bearing. Compared with single fault diagnosis, compound faults diagnosis is a challengeable task for the wind turbine mainshaft bearing. To extract fault features accurately, one fault diagnosis method based on Teager energy operator and second-order stochastic resonance (TSSR) is proposed. Firstly, Teager energy operator (TEO) is employed to achieve the purpose of enhancing impact energy in vibration signal. Then, under the assistance of residual noises, second-order stochastic resonance (SSR) is utilized for processed signal to enhance the fault features. Meanwhile, the modified signal-to-ratio (MSNR) index is carried out to select optimal parameters of SSR system automatically. Finally, the fault characteristic frequencies are identified by fast Fourier transform (FFT) spectrums of the SSR output. The test-rig artificial faulty bearing signal and wind turbine mainshaft bearing signal from wind farm are employed to illustrate the superiority and effectiveness of the proposed method. The contribution of the method provides a certain reference for the heavy-load and low-speed bearings fault diagnosis in other fields.