Abstract: Due to the influence caused by random noises and local strong disturbances embedded in signal on empirical mode decomposition (EMD) results, a novel integrated singular value decomposition-morphology filter method is proposed to overcome this shortcoming. And combining with EMD, a feature extraction method is presented. Firstly, reconstruct the original vibration signal in phase space and decompose the attractor track matrix by singular value decomposition (SVD), and then select a reasonable order for noise reduction according to the singular curve. Secondly, filter the de-noised signal by morphology filter. Finally, decompose it by EMD to extract the intrinsic mode functions (IMF) for fault feature extraction. Experimental results and industrial measurement analysi show that this method can extract fault characteristics of rolling bearing effectively, reduce decomposition levels and boundary effect of EMD, and imporve the timeliness and precision thereof.

Key words: Empirical mode decomposition, Fault feature extraction, Morphological filtering, Singular value decomposition