A fault frequency bands location method based on improved fast spectral correlation to extract fault features in axial piston pump bearings. (February 2021)

• Record Type: Journal Article
• Authors: Xiao, Chaoang; Tang, Hesheng; Ren, Yan; Xiang, Jiawei; Kumar, Anil
• Is Part Of: Measurement. Volume 171(2021)
• Publication Date: 2021-02
• Subjects: Axial piston pump -- Fault diagnosis -- Fast spectral correlation -- Fault frequency bands location -- Kurtosis enhanced spectral entropy -- Squared enhanced envelope spectrum; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2020.108734
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: A fault frequency bands location approach based on Fast-SC is developed. The kurtosis enhanced spectral entropy can identify the fault information. Squared enhanced envelope spectrum is applied to extract fault signatures. Experiment results demonstrate the feasibility of the proposed method. Abstract: 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.