Frequency-shift multiscale noise tuning stochastic resonance method for fault diagnosis of generator bearing in wind turbine

Abstract

The wind energy industry has developed very fast, while the condition monitoring and fault diagnosis for wind turbine is increasingly becoming the focus of manufacturers and wind-farm operators. However, due to the influence of harsh operation environment and complex internal structure of wind turbine, collected vibration signals are corrupted by strong noise. Effective extraction of useful feature information submerged in strong noise that is indicative of structural defects has remained a major challenge. In this paper, a novel frequency-shift multiscale noise tuning stochastic resonance (SR) method is proposed with the advantage of SR using noise to enhance weak signal features. Firstly, the frequency shift modulation algorithm is adopted to move the target signal to the designated low frequency domain. Then the obtained modulated signal is processed by the multiscale noise tuning algorithm based on discrete wavelet transform (DWT). The resulting output is used as the input of SR system. Finally, the tuning parameter of multiscale noise is determined by maximizing the modified signal-to-noise ratio (SNR) of system output. The proposed method can realize the feature enhancement and extraction of weak signal with arbitrary frequency. Experiments and fault diagnosis case of generator bearing in wind turbine validate the effectiveness of the proposed method.