Sparsity-enabled denoising method based on tunable Q-factor wavelet transform for bearing fault diagnosis

Fault diagnosis of rolling element bearings is of great importance to maintain the high reliability and long-term safe operation of rotating machinery. However, the weak fault feature is usually submerged in the heavy background noise, thus making it difficult to achieve the feature extraction. Therefore, the sparsity- enabled denoising method based on tunable Q-factor wavelet transform is proposed in this paper. Unlike the conventional wavelet transform, of which the Q-factor is constant, the TQWT can easily tune its Q-factor to match well with different oscillatory behavior of signals, thus achieving the fault feature extraction. The proposed method is applied to a simulated signal and the practical application in fault feature extraction of bearings. The processing result demonstrates that the proposed method can successfully extract the fault feature, showing that the method is more effective than the conventional wavelet transform method.