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Signal processing is of vital importance to the incipient fault diagnosis and the safety running of wind turbines. To adaptively eliminate noise and retain the underlying fault characteristic signal, an adaptive exponential wavelet threshold denoising method based on chaotic dynamic weight particle swarm optimization with sigmoid-based acceleration coefficients (SBAC-CDWPSO) is proposed in this paper. Firstly, a high-order continuous differentiable adaptive exponential threshold function (AETF) based on stein unbiased risk estimation is put forward to improve the defects of the traditional threshold functions. Secondly, the sine map and the sigmoid-based acceleration coefficients are applied for the velocity updating mechanism in particle swarm optimization (PSO). Meanwhile, the dynamic weight, the acceleration coefficient and the best-so-far position are introduced to update the new position with the previous position and the velocity in PSO. Moreover, the gaussian mutation strategy is added, which can effectively maintain the diversity of the swarm and get rid of local optimization. Thirdly, the SBAC-CDWPSO is used to optimize the threshold iteration process in AETF, which can greatly improve the iteration speed of the optimal threshold, and enhance the noise reduction effect. Experimental results showed that the signal-to-noise ratio of our proposed method was higher and the root mean square error was smaller comparing with the pre-existing algorithms. Moreover, wind turbine generator bearing fault diagnosis classification results illustrated that the fault diagnosis rate of the proposed denoising algorithm was up to 96.67%, indicating that the proposed method has great potential in the incipient fault diagnosis of wind turbine bearings.

Wind Turbine Bearing Incipient Fault Diagnosis Based on Adaptive Exponential Wavelet Threshold Function With Improved CPSO