基于振动烈度的液压泵故障多信息特征提取方法研究

刘思远1,2李晓明1,2刘建勋1,2张建姣1,2赵静一1,2

振动与冲击 ›› 2018, Vol. 37 ›› Issue (14) : 269-276.

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PDF(1933 KB)
振动与冲击 ›› 2018, Vol. 37 ›› Issue (14) : 269-276.
论文

基于振动烈度的液压泵故障多信息特征提取方法研究

  • 刘思远1,2李晓明1,2刘建勋1,2张建姣1,2赵静一1,2
作者信息 +

Multi-information fault feature extraction method for hydraulic pumps based on the vibration intensity

  • LIU Si-yuan1,2 , LI Xiao-ming1,2 , LIU Jian-xun1,2 , ZHANG Jian-jiao1,2 , ZHAO Jing-yi1,2
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文章历史 +

摘要

单一信息呈现出模糊性和不完备性,无法准确评估液压泵的工作状态。为此,提出一种基于振动烈度理论的多传感器信息特征提取方法。通过物理量转换法将滤波后的泵出口流量信号和压力信号分别转换成速度信号和加速度信号;利用振动烈度的频域计算方法提取振动、流量和压力信号的烈度特征因子;以滑靴磨损故障为例分析烈度特征因子的敏感性,找出对故障反映敏感的烈度因子。本研究对于增加信息完备性和提高状态评估准确率具有重要意义。

Abstract

The single information fault feature extraction usually shows the fuzziness and incompleteness, so that the working state of hydraulic pumps can not be evaluated accurately. Therefore, a multisensor information feature extraction method based on the vibration intensity theory was proposed. By means of the physical quantity conversion method, the filtered flow signals and pressure signals at the pump outlet were converted into speed signals and acceleration signals respectively. By using the frequency domain calculation method for the vibration intensity, the intensity factors of vibration, flow and pressure signals were extracted and the sensitivity of the intensity characteristic factors was analysed. The wear failure of a slipper was taken as an example ,and it is found the intensity factor is quite sensitive to faults. The study is of significance to increase the completeness of information and improve the accuracy of state assessment.
 

关键词

多传感器信息 / 轴向柱塞泵 / 振动烈度 / 特征提取 / 敏感性分析

Key words

Multi-sensor information / Axial piston pump / The vibration intensity / Feature extraction / Sensitivity analysis

引用本文

导出引用
刘思远1,2李晓明1,2刘建勋1,2张建姣1,2赵静一1,2. 基于振动烈度的液压泵故障多信息特征提取方法研究[J]. 振动与冲击, 2018, 37(14): 269-276
LIU Si-yuan1,2 LI Xiao-ming1,2 LIU Jian-xun1,2 ZHANG Jian-jiao1,2 ZHAO Jing-yi1,2. Multi-information fault feature extraction method for hydraulic pumps based on the vibration intensity[J]. Journal of Vibration and Shock, 2018, 37(14): 269-276

参考文献

[1] Kim B H,Na C W,Lee Y S,et al.Micro Electro-chemical Machining of 3D Micro Structure Using Dilute Sulfuric Acid [J]. Annals of the CIRP, 2005,54(1):191-194.
[2] 王昆,朱荻,张朝阳.微细电解线切割加工的基础研究[J].中国机械工程,2007,18(7):833-837.
Wang Kun, Zhu Di, Zhang Zhaoyang. Basic Research on Wire Electrochemical Micro-machining[J].China Mechanical Engineering, 2007,18 (7): 833-837.
[3] Zhu D, Wang K, Qu N S.Micro Wire Electrochemical Cutting by Using In Situ Fabricated Wire Electrode[J] .Annals of the CIRP, 2007,56(1):241-244.
[4] 姜万录, 刘思远. 多特征信息融合的贝叶斯网络故障诊断方法研究[J].中国机械工程,2010,21 ( 8 ) : 940-945/967.
Jiang Wanlu, Liu Siyuan. Study on Fault Diagnosis Method of Bayesian Network withMulti-feature InformationFusion[J]. ChinaMechanical Engineering, 2010,21 (8): 940-945/967.
[5] 唐曦凌, 梁霖, 高慧中,等. 结合连续小波变换和多约束非负矩阵分解的故障特征提取方法[J]. 振动与冲击, 2013, 32(19):7-11.
Tang Xiling, Liang Lin, Gao Huizhong, et al. Fault Feature Extraction Method Based on Continuous Wavelet Transform and Multi Constrained Nonnegative Matrix Factorization [J]. Vibration and Shock, 2013, 32 (): 7-11.
[6] 舒思材, 韩东. 基于多尺度最优模糊熵的液压泵特征提取方法研究[J]. 振动与冲击, 2016, 35(9):184-189.
Sicai Shu, Han Dong. Study on Feature Extraction of Hydraulic Pump Based on Multi Scale Optimal Fuzzy Entropy [J].Vibration and Shock, 2016, 35 (9): 184-189.
[7]李锋,林阳阳,晁苏全,等. 基于 CEEMDAN 与信息熵的液压泵故障特征提取方法研究[J].机床与液压,2016.44(16):192-195.
Li Feng, Lin Yangyang, Chao Suquan, et al. Study on Fault Feature Extraction Methodof Hydraulic PumpBased onCEEMDAN and Information Entropy [J]. Machine Tool and Hydraulics .2016.44 (16): 192 – 195.
[8] 王少萍,苑中魁,杨光琴. 液压泵信息融合故障诊断[J].中国机械工程, 2005,16(4):327-331.
Wang Shaoping, Yuan Zhongkui, Yang Guangqin. Study on Fault Diagnosisof Data Fusion in Hydraulic Pump [J]. China Mechanical Engineering, 2005,16 (4):327 -331
[9] 高英杰,孔祥东,张钦.基于小波包分析的液压泵状态监测方法[J].机械工程学报,2009,45(8):80-88.
Gao Yingjie, KongXiangdong, Zhang Qin. State Monitoring Method of Hydraulic Pump Based on Wavelet Packet Analysis [J]. Chinese Journal of Mechanical Engineering, 2009,45 (8): 80-88.
[10] Wang Y, Li H, Ye P. Fault Feature Extraction of Hydraulic Pump Based on CNC De-noising and HHT[J]. Journal of Failure Analysis and Prevention, 2015, 15(1):139-151.
[11] Liu W, Liu H Z. Research of Characteristics Extraction Based on Dynamic Pressure Signal[J].Applied Mechanics & Materials, 2013, 329:354-358.
[12] 樊新海,李胜利,安钢,等. 装甲车辆传动装置振动烈度监测与评估[J].兵工学报,2009,30(3):272-275.
Fan Xinhai, Li Shengli, An Gang, et al. Monitoring and Evaluation of Vibration Intensity of Armored Vehicle Transmission [J]. Journal of Ordnance, 2009,30 (3): 272-275.
[13] 徐小力, 许宝杰, 曹爱东,等. 虚拟仪器平台下的大型机组振动烈度预测模型研究[J]. 精密制造与自动化, 2003(S1):71-72.
Xu Xiaoli, Xu Baojie, Cao Aidong, et al.  Research on Vibration Intensity Prediction Model of Large Scale Unit Based on Virtual Instrument Platform [J]. Precision Manufacturing and Automation, 2003 (S1): 71-72.
[14]樊新海,赵智勇,安钢,等. 机械振动烈度的频域算法研究[J].装甲兵工程学院学报,2008,22(1):42-45.
Fan Xinhai, Zhao Zhiyong, Angang, et al.  Study on  Frequency Domain Algorithm of Mechanical Vibration Intensity[J]. Journal of Armored Force Engineering Institute, 2008, 22 (1): 42-45.
[15]刘大伟. 边坡稳定分析中常用参数敏感性分析[J]. 2007.
Liu Dawei. Sensitivity Analysis of Commonly Used Parameters in Slope Stability Analysis[J]. 2007.
[16]姜万录, 刘思远. 幅值域无量纲指标对液压泵故障敏感性
的实验研究[J]. 燕山大学学报, 2010, 34(5):383-389.
Jiang Wanlu, LiuSiyuan. Experimental Research on Sensitivity of Hydraulic Pump Fault by Dimensionless Index in Amplitude Domain[J]. Journal of Yanshan University, 2010, 34 (5): 383-389.
[17]刘思远. 信息融合和贝叶斯网络集成的故障诊断理论方法
及实验研究[D].燕山大学,2010.
LiuSiyuan. Fault DiagnosisTheoryMethodofInformation Fusion and Bayesian Network Integration Theory and Experimental Study[D].Yanshan University, 2010.

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