A fault frequency bands location method based on improved fast spectral correlation to extract fault features in axial piston pump bearings

In the working condition, the fault-excited impulses of axial piston pump bearings may be entirely buried by violent natural periodic impulses. Aiming at this problem, a fault frequency bands location method based on improved fast spectral correlation (Fast-SC) algorithm is proposed in this paper. Firstly, the Fast-SC is applied to analyze the original vibration signal to generate the cyclic spectral correlation image. Then, a new indicator named kurtosis enhanced spectral entropy (KESE) is exhibited to locate the fault frequency bands from the whole spectral frequency band, thereby highlighting the fault-excited impulses. Finally, the squared enhanced envelope spectrum (SEES) is employed to further extract feature frequencies and identify the fault type of the bearing. Experimental results validate the superiority and efficiency of the presented method. The fault-excited impulses extraction ability of the presented method is better than the traditional Fast-SC algorithm and ensemble empirical mode decomposition (EEMD) algorithm.

Automated summary

This paper proposes a fault frequency bands location method based on the Fast-SC algorithm, utilizing the new KESE indicator for fault frequency bands location and SEES for feature frequency extraction and fault type identification. Experimental results validate the superiority and efficiency of this method.