基于SGMD-Autogram的液压泵故障诊断方法研究

郑直1, 2,李显泽1,朱勇3,王宝中1

振动与冲击 ›› 2020, Vol. 39 ›› Issue (23) : 234-241.

PDF(1252 KB)
PDF(1252 KB)
振动与冲击 ›› 2020, Vol. 39 ›› Issue (23) : 234-241.
论文

基于SGMD-Autogram的液压泵故障诊断方法研究

  • 郑直1, 2,李显泽1,朱勇3,王宝中1
作者信息 +

Hydraulic pump fault diagnosis method based on SGMD-autogram

  • ZHENG Zhi1, 2, LI Xianze1, ZHU Yong3, WANG Baozhong1
Author information +
文章历史 +

摘要

辛几何模态分解方法(Symplectic Geometry Mode Decomposition, SGMD)存在特征信息分布过于分散问题、Autogram方法中的最大重复离散小波变换(Maximal Overlap Discrete Wavelet Packet Transform,MODWPT)存在特征提取能力不足问题,针对上述两问题,提出了基于SGMD-Autogram的新方法。首先,对实测液压泵多模态故障振动信号进行SGMD分解;其次,针对分解后产生的特征信息分布过于分散问题,提出基于最大无偏自相关谱峭度法,筛选含有丰富运行特征信息的模态分量为数据源,进而取代MODWPT,实现最优故障特征提取;最后,对数据源进行阈值处理,并基于频谱实现对液压泵故障的诊断。通过对比分析仿真和实测液压泵斜盘故障振动信号,验证了该方法可以有效地诊断斜盘故障。

Abstract

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.

关键词

液压泵 / 故障诊断 / 辛几何模态分解 / Autogram

Key words

hydraulic pump / fault diagnosis / symplectic geometry mode decomposition (SGMD) / Autogram

引用本文

导出引用
郑直1, 2,李显泽1,朱勇3,王宝中1. 基于SGMD-Autogram的液压泵故障诊断方法研究[J]. 振动与冲击, 2020, 39(23): 234-241
ZHENG Zhi1, 2, LI Xianze1, ZHU Yong3, WANG Baozhong1. Hydraulic pump fault diagnosis method based on SGMD-autogram[J]. Journal of Vibration and Shock, 2020, 39(23): 234-241

参考文献

[1] 杜伟, 房立清, 齐子元. 基于独立特征选择与流形学习的故障诊断[J]. 振动与冲击, 2018(16):77-82.
Du W, Fang L, Qi Z. Fault Diagnosis Based on Individual Feature Selection and Manifold Learning[J]. Journal of Vibration and Shock, 2018(16):77-82.
[2] 王余奎, 李洪儒, 许葆华. 基于LPP与VPMCD的液压泵故障模式识别[J]. 中国机械工程, 2015, 26(24):3327-3335.
Wang Y, Li H, Xu B. Fault Pattern Identification of Hydraulic Pump Based on VPMCD and LPP Algorithm. China Mechanical Engineering, 2015, 26(24):3327-3335.
[3] 李胜, 张培林, 吴定海, 等. 基于渐近式权值小波降噪和Adaboost算法的液压泵故障诊断[J]. 中国机械工程, 2011(9): 1067-1070.
Li S, Zhang P, Wu D. Fault Diagnosis for Hydraulic Pump Based on Gradual Asymptotic Weight Selection of Wavelet and Adaboost[J]. China Mechanical Engineering, 2011(9): 1067-1070.
[4] Lu C, Wang S, Zhang C. Fault Diagnosis Forpiston Pumps Based on a Two-Step EMD and Fuzzy C-Means Clustering[J]. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 2016, 230(16): 2913-2928.
[5] Jiang W, Zheng Z, Zhu Y, et al. Demodulation for Hydraulic
Pump Fault Signals Based on Local Mean Decomposition
and Improved Adaptive Multiscale Morphology Analysis[J].
Mechanical Systems & Signal Processing, 2015, s58-59:
179-205.
[6] 王浩天, 段修生, 单甘霖. 一种基于ILCD融合与多重分形去趋势波动分析的退化特征提取方法[J]. 振动与冲击, 2019, 38(6):233-238.
Wang H, Duan X, Shan G. Method for Degradation Feature Extraction Based on the ILCD Fusion and Multi-Fractal Detrended Fluctuation Analysis Journal of Vibration & Shock, 2019, 38(6):233-238.
[7] 王浩任, 黄亦翔, 赵帅,等. 基于小波包和拉普拉斯特征值映射的柱塞泵健康评估方法[J]. 振动与冲击, 2017, 36(22):45-50.
Wang H, Huang Y, Zhao S, et al. Health Assessment for Piston Pump based on WPD and LE[J]. Journal of Vibration & Shock, 2017, 36(22):45-50.
[8] 郑直, 姜万录, 王宝中,等. 改进AMD广义形态分形维数和KFCMC的液压泵故障诊断方法[J]. 振动与冲击, 2019, 38 (18): 46-52.
Zheng Z, Jiang W, Wang B, et al. Hydraulic Pump Fault Diagnosis Method Based on the Improved AMD Generalized Morphological Fractal Dimensions and Kernel Fuzzy C-Means Clustering[J]. Journal of Vibration & Shock, 2019, 38(18): 46-52.
[9] Lan Y, Hu J, Huang J, et al. Fault Diagnosis on Slipper Abrasion of Axial Piston Pump based on Extreme Learning Machine[J]. Measurement, 2018, 124: 378-385.
[10] Sun H, Yuan S, Luo Y. Cyclic Spectral Analysis of Vibration Signals for Centrifugal Pump Fault Characterization[J]. IEEE Sensors Journal, 2018, 18(7):2925-2933.
[11] Zhang, M, Jiang, Z, Feng, K. Research on Variational Mode Decomposition in Rolling Bearings Fault Diagnosis of the Multistage Centrifugal Pump[J]. Mechanical Systems & Signal Processing, 2017, 93(460):460-493.
[12] 周云龙, 吕远征. 基于多点噪声分析的离心泵早期气蚀故障诊断[J]. 振动与冲击, 2017, 36(7):39-44.
Zhou Y, Lv Y. Incipient Cavitations Fault Diagnosis for a Centrifugal Pump Based on Multi-position Noise Analysis[J]. Journal of Vibration & Shock, 2017, 36(7):39-44.
[13] Long W, Li X, Liang G, et al. A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method[J]. IEEE Transactions on Industrial Electronics, 2017, 99:1-1.
[14] Vásquez S, Kinnaert M, Pintelon R. Active Fault Diagnosis on a Hydraulic Pitch System Based on Frequency-Domain Identification[J]. IEEE Transactions on Control Systems Technology, 2017, 99:1-16.
[15] Son J, Kang D, Boo D, et al. An Experimental Study on The Fault Diagnosis of Wind Turbines Through a Condition Monitoring System[J]. Journal of Mechanical Science and Technology, 2018, 32(12):5573-5582.
[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[J]. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 2019, 41(2): 100.
[17] Pan H, Yang Y, Li X, et al. Symplectic Geometry Mode Decomposition and Its Application to Rotating Machinery Compound Fault Diagnosis[J]. Mechanical Systems & Signal Processing, 2019, 114:189-211.
[18] Moshrefzadeh A , Fasana A . The Autogram: An Effective Approach for Selecting the Optimal Demodulation Band in Rolling Element Bearings Diagnosis[J]. Mechanical Systems and Signal Processing, 2018, 105:294-318.

PDF(1252 KB)

Accesses

Citation

Detail

段落导航
相关文章

/