[1]陈保家,汪新波,赵春华,等.基于自适应局部迭代滤波和能量算子解调的滚动轴承故障特征提取[J].南京理工大学学报(自然科学版),2018,42(04):445.[doi:10.14177/j.cnki.32-1397n.2018.42.04.009]
 Chen Baojia,Wang Xinbo,Zhao Chunhua,et al.Fault feature extraction of rolling bearing based on ALIFand energy operator demodulation[J].Journal of Nanjing University of Science and Technology,2018,42(04):445.[doi:10.14177/j.cnki.32-1397n.2018.42.04.009]
点击复制

基于自适应局部迭代滤波和能量算子解调的滚动轴承故障特征提取()
分享到:

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

卷:
42卷
期数:
2018年04期
页码:
445
栏目:
出版日期:
2018-08-30

文章信息/Info

Title:
Fault feature extraction of rolling bearing based on ALIFand energy operator demodulation
文章编号:
1005-9830(2018)04-0445-08
作者:
陈保家12汪新波2赵春华12陈法法12邱光银2田红亮12
三峡大学 1.水电机械设备设计与维护湖北省重点实验室; 2.机械与动力学院,湖北 宜昌 443002
Author(s):
Chen Baojia12Wang Xinbo2Zhao Chunhua12Chen Fafa12Qiu Guangyin2Tian Hongliang12
1.Hubei Key Laboratory of Hydroelectric Machinery Design and Maintenance; 2.College of Mechanical and Power Engineering,China Three Gorges University,Yichang 443002,China
关键词:
自适应局部迭代滤波 本征模态函数 滚动轴承 能量算子 特征提取 经验模式分解 包络谱
Keywords:
adaptive local iterative filtering intrinsic mode function rolling bearing energy operator feature extraction empirical mode decomposition envelop spectrum
分类号:
TH17
DOI:
10.14177/j.cnki.32-1397n.2018.42.04.009
摘要:
为了提高滚动轴承的故障特征提取可靠性,该文提出了一种基于自适应局部迭代滤波(Adaptive local iterative filtering,ALIF)和能量算子解调的滚动轴承故障特征提取的方法。该方法首先利用ALIF将轴承的故障振动信号分解为若干个本征模态函数(Intrinsic mode function,IMF)分量,然后对包含故障信息最多的分量进行能量算子解调,得到分量的包络谱来提取轴承的故障特征。仿真结果表明:ALIF能够准确获取IMF分量,解决经验模式分解(Empirical mode decomposition,EMD)带来的模式混叠问题,结合能量算子解调方法能更好地凸显故障信号的包络谱特征,有效地提取轴承故障特征频率。
Abstract:
In order to improve the reliability of the fault feature extraction of rolling bearings,a method of fault feature extraction of rolling bearing based on adaptive local iterative filtering(ALIF)and energy operator demodulation is proposed. In this method,the fault vibration signal of bearing is first decomposed into several intrinsic mode function(IMF)components using ALIF,and the energy operator is demodulated for the component which contains the most fault information,and the envelope spectrum of the component is obtained to extract the fault characteristics of the bearing. Simulation results show that the ALIF can accurately obtain the IMF components and solve the problem of pattern aliasing caused by the empirical mode decomposition(EMD). Combining with the energy operator demodulation method,the characteristics of the envelope spectrum of the fault signal can be better highlighted and effectively extract bearing fault characteristic frequency.

参考文献/References:

[1] Jiang Hongkai,Li Chengliang,Li Huaxing. An improved EEMD with multiwavelet packet for rotating machinery multi-fault diagnosis[J]. Mechanical Systems and Signal Process,2013,36(2):225-239.
[2]彭辉燕. 基于HHT的故障诊断时频分析[D]. 成都:电子科技大学通信与信息工程学院,2010.
[3]Huang N E,Shen Zheng,Long Steven R,et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings of the Royal Society A,1998,454:903-995.
[4]程军圣,于德介,杨宇. 基于EMD的能量算子解调方法及其在机械故障诊断中的应用[J]. 机械工程学报,2004,40(8):115-118.
Chen Junsheng,Yu Dejie,Yang Yu. Based on EMD energy operator demodulation method and its application in mechanical fault diagnosis[J]. Journal of Mechanical Engineering,2004,40(8):115-118.
[5]李辉,郑海起,杨绍普. 基于EMD和Teager能量算子的轴承故障诊断研究[J]. 振动与冲击,2008,27(10):15-22.
Li Hui,Zhen Haiqi,Yang Shaopu. Research on bearing fault diagnosis based on EMD and Teager energy operator[J]. Journal of Vibration and Shock,2008,27(10):15-22.
[6]郑小霞,叶聪杰,符杨. 基于微分改进的EMD滚动轴承局部故障诊断[J]. 南京理工大学学报,2014,38(1):59-64.
Zheng Xiaoxia,Ye Congjie,Fu Yang. Localized fault diagnosis of rolling bearing using differential improved EMD[J]. Journal of Nanjing University of Science and Technology,2014,38(1):59-64.
[7]Wu Zhaohua,Huang N E. Ensemble empirical mode decomposition:a noise-assisted data analysis method[J]. Advances in Adaptive Data Analysis,2009,1(1):1-41
[8]Lin Luan,Wang Yang,Zhou Haomin. Iterative filtering as an alternative algorithm for empirical mode decomposition[J]. Advances in Adaptive Analysis,2009,1(4):543-560.
[9]Antonio C,Liu Jingfang,Zhou Haomin. Adaptive local iterative filtering for signal decomposition and instantaneous frequency analysis[J]. Applied and Computational Harmonic Analysis,2016,41(2):384-411.
[10]刘红星,陈涛,屈梁生. 能量算子解调方法及其在机械信号解调中的应用[J]. 机械工程学报,1998,34(5):85-90.
Liu Hongxing,Chen Tao,Qu Liangsheng. Energy operator demodulation method and its application in mechanical signal demodulation[J]. Journal of Mechanical Engineering,1998,34(5):85-90.
[11]何正嘉,陈进,王太勇. 机械故障诊断理论及应用[M]. 北京:高等教育出版社,2010.
[12]An Xueli,Zeng Hongtao,Li Chaoshun. Demodulation analysis based on adaptive local iterative filtering for bearing fault diagnosis[J]. Measurement,2016,94:554-560.

备注/Memo

备注/Memo:
收稿日期:2017-12-19 修回日期:2018-07-05
基金项目:国家自然科学基金(51605255,51775307); 湖北省重点实验室开放基金(2016KJX09,2016KJX15); 湖北省自然科学基金(2018CFB671)
作者简介:陈保家(1977-),男,博士,副教授,主要研究方向:现代信号处理,机械状态监测与故障诊断,可靠性评估与寿命预测,E-mail:cbjia@163.com;
通讯作者:陈法法(1983-),男,博士,副教授,主要研究方向:机电设备故障监测、诊断及可靠性评估,E-mail:316177108@qq.com。
引文格式:陈保家,汪新波,赵春华,等. 基于自适应局部迭代滤波和能量算子解调的滚动轴承故障特征提取[J]. 南京理工大学学报,2018,42(4):445-452. 投稿网址:http://zrxuebao.njust.edu.cn
更新日期/Last Update: 2018-08-30