Multiscale Filtering Reconstruction for Wind Turbine Gearbox Fault Diagnosis Under Varying-Speed and Noisy Conditions

One challenge in the gearbox fault diagnosis of variable-speed wind turbines via the popular vibration signal analysis is that, the signal spectrum is smeared by the time-varying shaft rotating speed and contaminated by some speed-unrelated components and noise. This paper proposes a new method, called multiscale filtering reconstruction, for wind turbine gearbox fault diagnosis under varying-speed and noisy conditions. The major contribution is that the new method can solve the spectrum smearing problem of the speed-related components while suppressing the speed-unrelated components and noise simultaneously. First, the time-variant vibration envelope signal is decomposed into time-variant monocomponent signals via a Vold-Kalman filter-based multiscale filtering algorithm. Then, the monocomponent signals are converted to be time-invariant by using a signal reform formula. Finally, a purified signal is reconstructed by synthesizing the reformed signals with different weighting factors. A gearbox fault diagnostic scheme based on the proposed method is developed for wind turbines equipped with doubly fed induction generators, in which the rotating frequency of the selected shaft used for multiscale filtering is estimated from the generator rotor current signals. Experimental studies on different gearbox faults are conducted to validate the proposed method and its superiority over the traditional angular resampling method.