Early bearing fault feature extraction based on CEMP time-frequency features

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Journal of Vibration and Shock ›› 2020, Vol. 39 ›› Issue (19) : 168-173.

ZHANG Zhiqiang1, MEI Jianmin2, ZHAO Huimin2, CHANG Chun2, SHEN Hong2

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Abstract

Aiming at the problem of weak early fault features and coupling of concurrent fault features of transmission bearings, a method based on cascade enhancement of MOMEDA and polar coordinates (CEMP) time-frequency features was proposed to extract early single and concurrent weak fault features of bearings. Teager energy operator of vibration signals was calculated to do first level enhancement of impact component energy. The multi-point optimal minimum entropy deconvolution amendment (MOMEDA) was used to do second level enhancement of impact information of fault feature period. The synchro-squeezing wavelet transform (SWT) was used to map SWT coefficients circularly into polar coordinate graph, and do third level enhancement of impact component of fault feature period in angle domain. The CEMP analysis was conducted for measured bearing signals under working conditions of normal, outer ring faults, inner ring faults and concurrent faults to verify the effectiveness of the proposed method. Results showed that the proposed method can extract weak and early features of single fault and concurrent ones of bearings with a high recognition rate.

Key words

bearing early fault / multi-point optimal minimum entropy deconvolution amendment (MOMEDA) / polar coordinates / Teager / feature extraction

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ZHANG Zhiqiang1, MEI Jianmin2, ZHAO Huimin2, CHANG Chun2, SHEN Hong2. Early bearing fault feature extraction based on CEMP time-frequency features[J]. Journal of Vibration and Shock, 2020, 39(19): 168-173

References

[1] 党建.大型旋转机械振动信号分析与早期故障辨识方法研究［D］.西安:西安理工大学，2018:4-7.
Dang Jian. Research on Vibration Signal Analysis and Early Fault Identification Method of Large Rotating Machinery [D]. Xi'an: Xi'an University of Technology, 2018:4-7.
[2] Lei Xiao, Xinghui Zhang, Siliang Lu, et al. A novel weak-fault detection technique for rolling element bearing based on vibrational resonance [J]. Journal of Sound and Vibration, 438 (2019) 490-505.
[3] D. Koulocheris, A. Stathis, Th. Costopoulos, et al. Wear and multiple fault diagnosis on rolling bearings using vibration signal analysis [J]. International Journal of Engineering Science Invention, 2014(4):11-19.
[4] 钟先友,田红亮，赵春华等. 基于迭代滤波和快速峭度图的滚动轴承微弱故障特征提取［J］.振动与冲击, 2018,37(9),190-195.
ZHONG Xian-you，TIAN Hong-liang，ZHAO Chun-hua, et al. Fault feature extraction for rolling bearings’weak faults based on iterative filtering and fast kurtogram [J]. Journal of Vibration and Shock, 2018,37(9),190-195.
[5] 杨晓雨,荆双喜,罗志鹏.基于LMS和Fast-Kurtogram的滚动轴承早期故障诊断［J］.噪声与振动控制,2019,39(1), 172-176.
YANG Xiao-yu, JING Shuang-xi, LUO Zhi-peng. Early Fault Diagnosis of Rolling Bearings based on LMS and Fast-Kurtogram[J].Noise and Vibration Control,2019,39(1), 172-176.
[6] 江星星,李舜酩.多共振频带自适应检测的轴承微弱故障诊断方法*［J］.电子测量与仪器学报,2016,30(4),526-533.
Jiang Xing-xing, Li Shun-ming. Bearing weak fault diagnosis method based on adaptive detection of multiresonance bands [J]. Journal of Electronic Measurement and Instrumentation, 2016, 30(4),526-533.
[7] 唐贵基,王晓龙.变分模态分解方法及其在滚动轴承早期故障诊断中的应用［J］.振动工程学报,2016,29(04):638-648.
TANG Gui-ji, WANG Xiao-long. Variational mode decomposition method and its application on incipient fault diagnosis of rolling bearing [J]. Journal of Vibration Engineering, 2016,29(04):638-648.
[8] 安雪君,郝如江,史云林.基于 SK－CICA和Teager能量算子的滚动轴承故障诊断方法［J］.轴承,2018,10:42－46.
AN Xue-jun, HAO Ru-jiang, SHI Yun-lin. Fault Diagnosis Method for Rolling Bearings Based on SK-CICA and Teager Energy Operator[J]. Bearing, 2018,10:42－46.
[9] MCDONALD G L,ZHAO Q.Multipoint optimal minimum entropy deconvolution and convolution fix: application to vibration fault detection［J］.Mechanical Systems and Signal Processing, 2017,82:461-477.
[10] 贾继德,张玲玲,江红辉等.基于对称极坐标法的变速器齿轮磨损故障诊断的研究*［J］.汽车工程, 2013,35 (1):93－97.
Jia Ji-de1，Zhang Ling-ling，Jiang Hong-hui，et al. A Study on the Wear Fault Diagnosis of Transmission Gears Based on Symmetric Polar Coordinates Method [J]. Automotive Engineering, 2013,35(1):93－97.
[11] 刘义亚,李可,陈鹏. 基于同步压缩小波变换的滚动轴承故障诊断［J］. 中国机械工程, 2018,29(05),585-590.
LIU Yi-ya, LI Ke, CHEN Peng. Fault Diagnosis for Rolling Bearings Based on Synchrosqueezing Wavelet Transform [J]. Journal of Vibration and Shock, 2018,29(05),585-590.
[12] 夏均忠,赵磊,白云川等.基于Teager能量算子和ZFFT的滚动轴承故障特征提取［J］.振动与冲击, 2017,36(11),106-110.
XIA Jun-zhong, ZHAO Lei, BAI Yunchuan, et al. Fault featureextraction of rolling element bearings based on Teager energy operator and ZFFT [J]. Journal of Vibration and Shock, 2017,36(11),106-110.
[13] 王志坚,张纪平,王俊元等.基于MED-MOMEDA的风电齿轮箱复合故障特征提取研究［J］.电机与控制学报,2018,22(9):111-118.
WANG Zhi-jian, ZHANG Ji-ping, WANG Jun-yuan,et al. Wind turbine gearbox multifault diagnosis based on MED-MOMEDA [J]. Electric Machines and Control, 2018,22(9):111-118.
[14] 于明奇,夏均忠,白云川等.基于格点搜索法的MOMEDA 在滚动轴承故障特征提取中的应用［J］.军事交通学院学报,2018,30(3),50-55.
YU Ming-qi, XIA Jun-zhong, BAI Yun-chuan, et al. Application of MOMEDA in Ｒolling Bearing Fault Feature Extraction Based on Grid Search Method [J].Journal of Military Transportation University, 2018,30(3),50-55.
[15] 武超,孙虎儿,梁晓华.基于MOMEDA和包络谱的齿轮微弱故障特征提取［J］.机械传动,2018,3:164 -169.
Wu Chao, Sun Huer, Liang Xiao-hua. Feature Extraction of Weak Fault for Gear based on MOMEDA and Envelope Spectrum[J]. Journal of Mechanical Transmission, 2018,3:164 -169.
[16] 梅检民,肖云魁,赵慧敏等.基于Teager算子的极坐标时频特征增强算法提取轴承微弱故障特征［J］.振动工程学报,2014,27(01):152-158.
Mei Jian-min, Xiao Yun-kui, Zhao Hui-min, et al. Enhancement of polar time-frequency feature based on Teager energy operator for extraction of bearing weak fault [J]. Journal of Vibration Engineering, 2014,27(01):152-158.
[17] 李继猛,黄梦君,谢平等.同步压缩-交叉小波变换及滚动轴承故障特征增强［J］.计量学报,2018,02:237- 241.
LI Ji-meng, HUANG Meng-jun, XIE Ping. Synchrosqueezing-cross Wavelet Transform and Enhanced Fault Diagnosis of Rolling Bearing [J]. Acta Metrologica Sinica, 2018,02:237-241.