Hydraulic pump fault diagnosis method of symplectic geometry mode decomposition and generalized morphological fractal dimensions

被引：0

作者：

Zheng Z. ^{[1
]}

Wang B. ^{[1
]}

Liu J. ^{[1
]}

Jiang W. ^{[2
,3
]}

机构：

[1] College of Mechanical Engineering, North China University of Science and Technology, Tangshan

[2] Hebei Provincial Key Laboratory of Heavy Machinery Fluid Power Transmission and Control, Yanshan University, Qinhuangdao

[3] Key Laboratory of Advanced Forging & Stamping Technology and Science, Ministry of Education of China, Yanshan University, Qinhuangdao

来源：

Harbin Gongcheng Daxue Xuebao/Journal of Harbin Engineering University | 2020年 / 41卷 / 05期

关键词：

Fault diagnosis; Feature extraction; General morphological fractal dimensions; Hydraulic pump; Loose slipper fault; Mode energy; Slipper wear fault; Symplectic geometry mode decomposition;

D O I：

10.11990/jheu.201902039

中图分类号：

学科分类号：

摘要：

Aiming at the fault diagnosis of hydraulic pumps, we propose a new fusion method based on symplectic geometry mode decomposition (SGMD) and general morphological fractal dimensions (GMFDs). First, SGMD is applied to decompose the multi-mode vibration fault signals of the hydraulic pump. Second, the modes with rich running feature information can be selected by the proposed energy method, and they are restructured as data sources. Lastly, GMFDs are extracted from the data source, and hydraulic pump faults can be diagnosed. The simulation signals and actually measured fault signals of the vibrating hydraulic pump were compared, and the ability of the proposed method to effectively diagnose hydraulic pump faults was verified. Copyright ©2020 Journal of Harbin Engineering University.

引用

页码：724 / 730

页数：6

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