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

PDF(2545 KB)

振动与冲击 ›› 2016, Vol. 35 ›› Issue (13) : 134-139.

论文

马增强，李亚超, 刘政，谷朝健

作者信息 +

Rolling bearing fault feature extraction based on variational mode decomposition and Teager energy operator

Ma Zeng-qiang, Li Ya-chao, Liu Zheng, Guang Chao-jian

Author information +

文章历史 +

摘要

针对滚动轴承早期故障振动信号信噪比低、故障特征提取困难的问题，提出了基于变分模态分解和能量算子的滚动轴承故障特征提取方法。该方法首先对故障信号进行变模态分解(variational mode decomposition, VMD)，得到若干本征模态分量(intrinsic mode function, IMF)；其次，通过峭度准则选取其中峭度最大的分量进行Teager能量算子解调，得到信号的Teager能量谱。将该方法应用到滚动轴承仿真故障数据和实际数据中，结果表明，该方法提高了信号的分解效率，降低了噪声的影响，能够实现滚动轴承故障的精确诊断，证明了该方法的有效性。

Abstract

In order to solve the problems that the fault feature of rolling bearing in early failure period is difficult to extract, an incipient fault diagnosis method for rolling bearing based on variational mode decomposition (VMD) and Teager energy operator was proposed. Firstly, VMD was used to decompose the fault signal into several intrinsic mode functions (IMFs), and then the IMF of biggest kurtosis was selected with Kurtosis Criterion and demodulated into Teager energy spectrum with Teager energy operator. The proposed method was applied to simulated signals and actual signals. The results show that this method improves the efficiency of signal decomposition and reduces the effect of noise, enabling accurate diagnosis of rolling bearing fault, the analysis results demonstrated the effectiveness of the proposed method.

导出引用

马增强，李亚超, 刘政，谷朝健. 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, Guang Chao-jian. Rolling bearing fault feature extraction based on variational mode decomposition and Teager energy operator[J]. Journal of Vibration and Shock, 2016, 35(13): 134-139

参考文献

[1] Wu Z H, Huang N E. Ensemble Empirical Mode Decomposition: a noise-assisted data analysis method [J]. Advances in Adaptive Data Analysis, 2009, 1(1): 1-41.
[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，2008,27(10):15-22.
Li Hui, Zhang Hai-qi, Yang Shao-pu. Bearing fault diagnosis based on EMD and Teager Kaiser energy operator [J]. Journal of vibration and shock, 2008, 27(10): 15-22.
[3] Kedadouche, M; Thomas, M; Tahan, A. A hybrid method combining Teager Kaiser energy operator, empirical mode decomposition and minimum entropy deconvolution for monitoring gears damages [J]. Mechanics & Industry, 2015, 16(6): 1-14.
[4] 张玲玲，廖红云，曹亚娟，等．基于 EEMD 和模糊 C均值聚类算法诊断发动机曲轴轴承故障[J]．内燃机学报，2011，29(4)：332-336．
    Zhang Ling-ling, Liao Hong-yun, Cao Ya-juan, et al. Diagnosis on crankshaft bearing fault based on EEMD and fuzzy C mean clustering arithmetic [J]. Transactions of CSICE, 2011, 29(4)：332-336.
[5] A. Tabrizi; L. Garibaldi; A. Fasana. Early damage detection of roller bearings using wavelet packet decomposition, ensemble empirical mode decomposition and support vector machine [J]. Meccanica, 2015, 50(3): 865-874.
[6] 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. Roller bearing fault diagnosis method based on LMD and neural network [J]. Journal of vibration and shock, 2010, 29(8): 141-144.
[7] Dong. Z. S, Tian. X. Q, Zeng. J. C. Mechanical fault diagnosis based on LMD-approximate entropy and LSSVM [J]. Telkomnika - Indonesian Journal of Electrical Engineering, 2013,11(2): 803-808.
[9] Konstantin Dragomiretskiy, Dominique Zosso. Variational Mode Decomposition [J]. Transactions on Signal Processing, 2013, 10 (1109):1-15.
[10] Potamianos A, Maragos P. A comparison of the energy operator and Hilbert transform approaches for signal and apeech demodulation [J]. Signal Processing, 1994, 37(1): 95-120.
[11] 刘长良，武英杰，甄成刚. 基于变分模态分解和模糊C均值聚类的滚动轴承故障诊断[J].中国电机工程学报,2015,35 (0):1-7.
     Liu Changliang, Wu Yingjie, Zhen Chenggang. Rolling bearing fault diagnosis based on variational mode decomposition and fuzzy C means clustering [J]. Proceedings of the CSEE, 2015, 35 (0): 1-7.
[12] 唐贵基，王晓龙.参数优化变分模态分解方法在滚动轴承早期故障诊断中的应用[J].西安交通大学学报,2015,49(5):73- 81.
     Tang Guiji, Wang Xiaolong. Parameter optimized variational mode decomposition method with application to incipient fault diagnosis of rolling bearing [J].Journal of Xi’an Jiaotong University, 2015, 49(5): 73- 81.