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

PDF(2425 KB)

Journal of Vibration and Shock ›› 2016, Vol. 35 ›› Issue (3) : 7-13.

Wu Zhe1，Yang Shao-pu2，Zhang Jian-chao1,2

Author information +

History +

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.

Key words

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

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

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

References

[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.