A weighted multi-scale dictionary learning model and its applications on bearing fault diagnosis

Extracting impulsive information under strong background noise and harmonic interference is a challenging problem for bearing fault diagnosis. Multi-scale transforms have achieved great success in extracting impulsive feature information, however, how to choose a suitable transform is a difficult problem, especially in the case of strong noise interference. Therefore, dictionary learning methods have attracted more and more attention in recent years. A weighted multi-scale dictionary learning model (WMSDL) is proposed in this paper which integrates the multi-scale transform and fault information into a unified dictionary learning model and it successfully overcomes four disadvantages of traditional dictionary learning algorithms including lacking the multi-scale property; restricting training samples to local patches; being sensitive to strong harmonic interference; suffering from high computational complexity. Moreover, algorithmic derivation, computational complexity and parameter selection are discussed. Finally, The effectiveness of the proposed method is verified by both the numerical simulations and experiments. Comparisons with other state-of-the-art methods further demonstrate the superiority of the proposed method.