基于LMD自适应多尺度形态学和Teager能量算子方法在轴承故障诊断中的应用

武哲1,杨绍普2,张建超1,2

振动与冲击 ›› 2016, Vol. 35 ›› Issue (3) : 7-13.

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振动与冲击 ›› 2016, Vol. 35 ›› Issue (3) : 7-13.
论文

基于LMD自适应多尺度形态学和Teager能量算子方法在轴承故障诊断中的应用

  • 武哲1,杨绍普2,张建超1,2
作者信息 +

Bearing fault feature extraction method based on LMD adaptive multiscale morphological and energy operator demodulating

  • Wu Zhe1,Yang Shao-pu2,Zhang Jian-chao1,2
Author information +
文章历史 +

摘要

为了从故障轴承信号中提取包含故障信号的特征频率,提出了基于LMD自适应多尺度形态学和Teager能量算子解调的方法。首先,采用LMD将目标信号分解成有限个PF(Product function,PF)分量,分别对其进行多尺度形态学滤波,利用峭度准则优化形态学结构元素尺度,自适应寻求最优解,最后用Teager能量算子计算各PF分量的瞬时幅值,通过瞬时Teager能量的Fourier频谱识别轴承的故障特征频率。为了验证理论的正确性,进行了数字仿真实验和轴承故障模拟实验,并与EMD形态学和包络解调方法进行了比较,结果表明该算法明显优于其他两种方法,对滚动轴承外圈、内圈和滚子故障的检测精度更高,能够清晰地提取出故障信号的频率特征。

Abstract

In order to extract the characteristic frequencies from bearings fault signals containing fault information, the adaptive morphology method was proposed based on LMD and energy operator demodulating. LMD was used to decompose multi-component AM-FM signal into number of production functions(PF) firstly, the PFs containing fault information were multiscale morphologically filtered respectively. The kurtosis criterion was used to adaptive optimize the structural elements of morphology, Finally, the energy energy operator demodulating is applied to each PF and the amplitudes and frequencies of a multi-componentAM-FM signal are extracted for bearing fault diagnosis. In order to verify the correctness of the theory, through numerical simulations and bearings fault simulation tests,the proposed method was compared with EMD and envelope demodulation. The results showed that the proposed method is superior to the other two, the higher detection accuracy of the outer and inner ring of rolling bearing and roller faults, it can be used to clearly extract various characteristic frequencies of bearings faults.

关键词

滚动轴承 / LMD / 多尺度形态学 / 故障诊断 / Teager能量算子

Key words

roller bearing / LMD / multiscale morphology / fault diagnosis / Energy operator demodulating

引用本文

导出引用
武哲1,杨绍普2,张建超1,2. 基于LMD自适应多尺度形态学和Teager能量算子方法在轴承故障诊断中的应用[J]. 振动与冲击, 2016, 35(3): 7-13
Wu Zhe1,Yang Shao-pu2,Zhang Jian-chao1,2. Bearing fault feature extraction method based on LMD adaptive multiscale morphological and energy operator demodulating[J]. Journal of Vibration and Shock, 2016, 35(3): 7-13

参考文献

[1] Wu Zhicheng, Wang Chongyang, Ren Aijun. Optimal selection of wavelet base functions for eliminating signal trend based on wavelet analysis [J]. Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology, 2013, 33: 811-814.
[2] 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,2008,10:15-18.
Li Hui, Zheng Haiqi, Yang Shaopu, Bearing fault diagnosis based on emd and teager Kaiser energy operartor [J]. Journal of Vibration and Shock, 2008, 10: 15-18.
[3] Liu Xiaofeng, Bo Lin, Luo Honglin. Bearing faults diagnostics based on hybrid LS-SVM and EMD method measurement [J]. Journal of the international measurement confederation, 2015, 59: 145-166.
[4] 程军圣,史美丽,杨宇.基于LMD与神经网络的滚动轴承故障诊断方法[J].振动与冲击,2010,29(8):141-144.
    Cheng Jun-sheng, Shi Mei-li, Yang Yu. Bearing fault diagnosis method LMD and neural network [J]. Journal of Vibration and Shock, 2010, 29(8): 141-144.
[5] Qin S R, Zhong Y M. A new algorithm of Hilbert Huang transform [J]. Mechanical Systems and Signal Processing, 2006, 20(8): 1941-1952.
[6] Huang N E, Wu M L, Long S R. A confidence limit for the empirical mode decomposition and Hilbert spectral analysis [J]. Proc. R. Soc. Lond. A, 2003, 459: 2317-2345.
[7] Smith J S.The local mean decomposition and its application to EEG perception data [J]. Journal of the Royal Society Interface, 2005, 2(5): 443-454.
[8] 鞠萍华,秦树人,赵玲.基于LMD的能量算子解调方法及其在故障特征信号提取中的应用[J].振动与冲击,2011,30(2):1-4.
Ju Ping-hua, Qin Shu-ren, Zhao Ling. Energy operator demodulating approach on LMD and its application in extracting characteristics of a fault signal [J]. Journal of Vibration and Shock, 2011, 30(2): 1-4.
[9] 李慧梅,安钢,黄梦.基于局部均值分解的边际谱在滚动轴承故障诊断中的应用[J].振动与冲击,2014,33(3):5-8.
Li Hui-mei, An Gang, Huang Meng. Application of marginal spectrum based on local mean decomposition in rolling bearing fault diagnosis.[J]. Journal of Vibration and Shock, 2014, 33(3): 5-8.
[10] Serra J.Morphological filtering:an overwiew [J].Signal Process,1994,38(1): 3-11.
[11] 李兵,张培林,米双山,等.机械故障信号的数学形态学分析与智能分类.北京:国防工业出版社,2011.
   Li Bing, Zhang Pei-lin, Mi Shuang-shan, etal. Morphological analysis and intelligent classification Mechanical fault signal. Beijing: National Defense Industry Press, 2011.
[12] 郝如江,卢文秀,褚福磊.形态滤波器用于滚动轴承故障信号的特征提取[J].中国机械工程,2009,20(2):197-201.
HAO Ru-jiangm, LU Wen-xiu, CHU Fu-lei. Morphological filters in feature extraction for rolling bearing defect signals [J]. China Mechanical EnginLring, 2009, 20(2): 197-201.
[13] 郝如江,卢文秀,褚福磊.滚动轴承故障信号的多尺度形态学分析[J].机械工程学报,2008,44(11):160-165.
HAO Rujiang, LU Wenxiu, CHU Fulei. Multiscale morphological analysis on fault signals of rolling element bearing [J]. Chinese Journal of Mechanical EnginLring, 2008, 44(11):160-165.
[14] Maragos P, Kaiser J F, Quatieri T F. Energy separation in signal modulation with application to speech analysis [J]. IEEE Transactions on Signal Processing, 1993, 41(10): 3024-3051.
[15] Alexandros P, Petros M. A comparison of the energy operator and the Hilbert transform approach to signal and speech demodulation [J].Signal Processing, 1994, 37(1): 95-120.
[16] 王天金,冯志鹏,郝如江,褚福磊.基于Teager能量算子的滚动轴承故障诊断研究[J].振动与冲击,2012,31(2):1-6.
Wang Tian-jin, Feng Zhi-peng, Hao, Ru-jiang, Chu Fulei. Fault diagnosis of rolling ele ment bearings based on Teager energy operator [J]. Journal of Vibration and Shock, 2012, 31(2): 1-6.
[17] Harris M C, Blotter J D, Scott D. Sommerfeldt obtaining the complex pressure field at the hologram surface for use in near-field acoustical holography when pressure and in-plane velocities are measured [J]. The Journal of the Acoustical Society of America, 2006 119(2): 808-816.
[18] Zhang L, Xu J, etal. Multiscale morphology analysis and its application to fault diagnosis [J]. Mechanical Systems and Signal Processing, 2008, 22(3): 597-610.

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