Segmentation MED method based on kurtosis-frequency curve and its application in bearing diagnosis

Signature extraction of fault impacts is a significant task for rolling bearing diagnosis. A series of blind source and target deconvolution methods respectively represented by minimum entropy deconvolution (MED) and maximum correlated kurtosis deconvolution (MCKD) are proposed to enhance the fault impulses from the complex interference components. However, blind source deconvolution methods are usually fragile for strong shock interference and target deconvolution methods require a rigorous high-accuracy prior knowledge. In addition, many methods based on correlation kurtosis indicator are no longer applicable under the variable speed condition. These problems form a huge obstacle to their application in fault diagnosis of rolling bearing. To overcome these limitations, a segmentation MED guided by the kurtosis-frequency curve is proposed in this paper. The presented method calculates the kurtosis-frequency curve by selecting appropriate filter parameters. Signals are adaptively segmented to several parts corresponding to the actual component according to the kurtosis-frequency curve. MED for each segmentation can effectively extract fault signatures and reduce the influence of other interference components. Compared with MED, the new method is more robust for strong interference impacts. Compared with the MCKD, this method does not need a high-accuracy input parameter and can woks at variable speed. Its effectiveness has been verified by several simulation signals and actual railway bearing datasets.