基于VMD和1.5维Teager能量谱的滚动轴承故障特征提取

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振动与冲击 ›› 2017, Vol. 36 ›› Issue (18) : 98-104.

论文

向玲，张力佳

作者信息 +

Rolling bearing fault feature extraction based on VMD and 1.5-dimensional Teager energy spectrum

Xiang Ling, Zhang Lijia

Author information +

文章历史 +

摘要

为准确提取非线性、非平稳的滚动轴承故障信号中的故障特征，提出基于变分模式分解（Variational Mode Decomposition, VMD）和1.5维Teager能量谱的滚动轴承故障特征提取方法。变分模式分解（VMD）是一种新的信号自适应分解方法，1.5维Teager能量谱具有1.5维谱良好的降噪效果和Teager能量算子强化信号瞬态冲击的优点。故障特征提取过程：首先，对滚动轴承故障信号进行VMD分解得到一组分量，根据峭度-相关系数准则筛选出2个冲击特征明显分量进行信号重构；再次，对重构信号进行1.5维Teager能量谱分析；最后根据能量谱图的分析，提取出滚动轴承的内圈和滚动体故障特征。仿真信号和实验信号的分析都验证了所提方法的有效性。通过与EEMD分解比较，采用VMD变分模式分解和1.5维Teager能量谱的分析方法更具有区分性，可以有效识别滚动轴承的故障特征。

Abstract

Rolling bearing fault signal is nonlinear and non-stationary. Combine variational mode decomposition (VMD) with 1.5-dimensional Teager energy spectrum, the purpose of rolling bearing fault diagnosis can be achieved. VMD is a new method of adaptive signal decomposition. 1.5-dimensional Teager energy spectrum not only has the advantage of 1.5-dimensional denoise, but also strengthens transient impact feature signal using Teager operator. Firstly, the rolling bearing fault signal was decomposed using VMD. The two components, which had obvious impact features, were extracted and reconstructed using the kurtosis-correlation coefficient criteria. Secondly, the reconstructed signal was analyzed using the 1.5-dimensional Teager energy spectrum. Lastly, according to the energy spectrum analysis of the reconstructed signal, the inner ring and rolling element fault features were extracted. The analysis of the simulation signal and the test signal verifies the effectiveness of the proposed method. Compared with ensemble empirical mode decomposition, the proposed method would be more distinctive and effectively identify fault feature of the rolling bearing.

导出引用

向玲，张力佳. 基于VMD和1.5维Teager能量谱的滚动轴承故障特征提取[J]. 振动与冲击, 2017, 36(18): 98-104

Xiang Ling, Zhang Lijia. Rolling bearing fault feature extraction based on VMD and 1.5-dimensional Teager energy spectrum[J]. Journal of Vibration and Shock, 2017, 36(18): 98-104

参考文献

[1]. 隋文涛，张丹，Wilson Wang. 基于EMD和MKD的滚动轴承故障诊断方法[J]. 振动与冲击，2015,34(9):55-59.
SUI Wen-tao, ZHANG Dan, Wilson Wang. Fault diagnosis of rolling element bearings based on EMD and MKD [J]. Journal of vibration and shock, 2015, 34(9): 55-59.
[2]. 张亢，程军圣. 基于局部均值分解和峭度图的滚动轴承包络分析方法[J]. 航空动力学报，2015, 30(12):3043-3050.
ZHANG Kang, CHENG Jun-sheng. Roller bearing envelope analysis method based on local mean decomposition and kurtogram [J]. Journal of aerospace power, 2015, 30(12): 3043-3050.
[3]. 钟先友，曾良才，赵春华. 局域均值分解和1.5维谱在机械故障诊断中的应用[J]. 中国机械工程，2013，24(2)：452-457.
ZHONG Xian-you, ZENG Liang-cai, ZHAO Chun-hua. Application of local mean decomposition and 1.5 dimension spectrum in machinery fault diagnosis [J]. Chinese mechanical engineering, 2013, 30(2): 452-457.
[4]. 胡爱军，马万里，唐贵基. 基于集成经验模态分解和峭度准则的滚动轴承故障特征提取方法[J]. 中国电机工程学报，2012,32(11):106-111+153..
HU Ai-jun, MA Wan-li, TANG Gui-ji. Rolling bearing fault feature extraction method based on ensemble empirical mode decomposition and Kurtosis criterion[J]. Proceedings of the CSEE. 2012,32(11):106-111+153.
[5]. 唐贵基，王晓龙. 基于EEMD降噪和1.5维能量谱的滚动轴承故障诊断研究[J]. 振动与冲击，2014, 33(1):6-10.
TANG Gui-ji, WANG Xiao-long. Fault diagnosis for roller bearings based on EEMD de-noising and 1.5-dimensional energy spectrum[J]. Journal of vibration and shock, 2014, 33(1): 6-10.
[6]. 陈略，訾艳阳，何正嘉，等. 噪声协助的EMD-1.5维谱信号抗混分解与特征提取[J]. 振动与冲击， 2010,29(5):26-30.
CHEN Lue, ZI Yan-yang, HE Zheng-jia, et al. Noise-assisted EMD-1.5 dimension spectrum for signal anti-alias decomposition and feature extraction[J]. Journal of vibration and shock, 2010, 29(5): 26-30.
[7]. 向玲，鄢小安. 汽轮机转子故障诊断中LMD法和EMD法的性能对比研究[J]. 动力工程学报，2014,34(12):945-951.
XIANG Ling, YAN Xiao-an. Performance contrast between LMD and EMD in fault diagnosis of turbine rotors[J]. Journal of Chinese society of power engineering, 2014, 34(12): 945-951.
[8]. Dragomiretskiy K, Zosso D． Variational mode decomposition[J]． IEEE Tran on Signal Processing， 2014，62(3)：531-544．
[9]. 苏文胜，王奉涛，张志新，等. EMD降噪和谱峭度法在滚动轴承早期故障诊断中的应用[J]. 振动与冲击，2010,29(3):18-21+201.
SU Wen-sheng, WANG Feng-tao, ZHANG Zhi-xin, et al. Application of EMD denoising and spectral kurtosis in early fault diagnosis of rolling element bearings[J]. Journal of vibration and shock. 2010,29(3):18-21+201.
[10]. 刘长良，武英杰，甄成刚. 基于变分模态分解和模糊C聚类均值聚类的滚动轴承故障诊断[J]. 中国电机工程学报，2015,35(13):3358-3365.
LIU Chang-liang, WU Ying-jie, ZHEN Cheng-gang. Rolling bearing fault diagnosis based on variational mode decomposition and fuzzy C means clustering [J]. Proceedings of the CSEE. 2015,35（13）:3358-3365
[11]. Salim L. Comparative study of signal denoising by wavelet threshold in empirical and variational mode decomposition domains[J]. Healthcare Technology Letters, 2014,1(3): 104‐109.
[12]. Aneesh C, Sachin K, Hisham P M, et al. Performance comparison of variational mode decomposition over empirical wavelet transform for the classification of power quality disturbances using support vector machine[J]. Procedia computer science, 2015,46: 372‐380.
[13]. 马增强，李亚超，刘政，等. 基于变分模态分解和Teager能量算子的滚动轴承故障特征提取[J]. 振动与冲击，2016,35(13):134-139.
MA Zeng-qiang, LI Ya-chao, LIU Zheng, et al. Rolling bearings’ fault feature extraction based on variational mode decomposition and Teager energy operator[J]. Journal of vibration and shock. 2016,35(13):134-139.