Hydraulic pump fault diagnosis method based on SGMD-autogram

被引：0

作者：

Zheng Z. ^{[1
,2
]}

Li X. ^{[1
]}

Zhu Y. ^{[3
]}

Wang B. ^{[1
]}

机构：

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

[2] HUIDA Sanitary Ware Co., Ltd., Tangshan

[3] Research Center of Fluid Machinery Engineering and Technology, Jiangsu University, Zhenjiang

来源：

Zhendong yu Chongji/Journal of Vibration and Shock | 2020年 / 39卷 / 23期

关键词：

Autogram; Fault diagnosis; Hydraulic pump; Symplectic geometry mode decomposition (SGMD);

D O I：

10.13465/j.cnki.jvs.2020.23.033

中图分类号：

学科分类号：

摘要：

The symplectic geometry mode decomposition (SGMD) method has the problem of feature information distribution being too scattered, and Autogram method has the problem of the feature extraction ablility of the maximal overlap discrete wavelet packet transform (MODWPT) being not strong. Here, aiming at above problems, a new method based on SGMD and Autogram was proposed. Firstly, SGMD was applied to decompose measured multi-mode vibration fault signals of hydraulic pump. Secondly, aiming at the problem of too scattered feature information distribution after decomposition, the spectral kurtosis method based on maximum unbiased autocorrelation was proposed. Some mode components containing rich operating feature information were screened out as the data source to replace MODWPT, and realize the extraction of the optimal fault features. Finally, the data source was processed with threshold, and the fault diagnosis of hydraulic pump was realized based on spectrum. By comparing and analyzing simulated and measured vibration fault signals of hydraulic pump swash plate, it was verified that the proposed method can effectively diagnose hydraulic pump swash plate faults. © 2020, Editorial Office of Journal of Vibration and Shock. All right reserved.

引用

页码：234 / 241

页数：7

共 18 条

[11]

ZHANG M, JIANG Z, FENG K., Research on variational mode decomposition in rolling bearings fault diagnosis of the multistage centrifugal pump, Mechanical Systems and Signal Processing, 93, 460, pp. 460-493, (2017)

[12]

ZHOU Yunlong, LU Yuanzheng, Incipient cavitations fault diagnosis for a centrifugal pump based on multi-position noise analysis, Journal of Vibration and Shock, 36, 7, pp. 39-44, (2017)

[13]

LONG W, LI X, LIANG G, Et al., A new convolutional neural network-based data-driven fault diagnosis method, IEEE Transactions on Industrial Electronics, 99, pp. 5990-5998, (2017)

[14]

VASQUEZ S, KINNAERT M, PINTELON R., Active fault diagnosis on a hydraulic pitch system based on frequency-domain identification, IEEE Transactions on Control Systems Technology, 99, pp. 1-16, (2017)

[15]

SON J, KANG D, BOO D, Et al., An experimental study on the fault diagnosis of wind turbines through a condition monitoring system, Journal of Mechanical Science and Technology, 32, 12, pp. 5573-5582, (2018)

[16]

ZHU Y, TANG S, QUAN L, Et al., Extraction method for signal effective component based on extreme-point symmetric mode decomposition and kullback-leibler divergence, Journal of the Brazilian Society of Mechanical Sciences and Engineering, 41, 2, (2019)

[17]

PAN H, YANG Y, LI X, Et al., Symplectic geometry mode decomposition and its application to rotating machinery compound fault diagnosis, Mechanical Systems and Signal Processing, 114, pp. 189-211, (2019)

[18]

MOSHREFZADEH A, FASANA A., The autogram: An effective approach for selecting the optimal demodulation band in rolling element bearings diagnosis, Mechanical Systems and Signal Processing, 105, pp. 294-318, (2018)

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