Application of EMD-ICA to Fault Diagnosis of Rolling Bearings

Abstract

Abstract:

Vibration signals generated by incipient faults of rolling element bearing was usually with low energy and dispersed frequency distribution, they were easily merged in strong disturbances of other vibration sources．According to this feature of rolling bearing's vibration signals, a new fault-diagnosis approach based on the combination of EMD and ICA was proposed. With the approach, single channel vibration signals were decomposed using EMD method and modal components were obtained at first, then they were restructured based on mutual correlation criterion. Therefore multi-dimensional virtual noise channels were constructed, source signals and noise signals were separated when FastICA method was adopted with multi-dimensional virtual noise channels used as input matrix of ICA. The method was applied to fault diagnosis of rolling bearings. The denoised signals were analyzed by fast Fourier transform method, then the results were compared with the fault characteristics to judge operation state of the rolling element bearings. Simulations and tests verify the feasibility of the method.

Key words: empirical mode decomposition(EMD), independent component analysis(ICA), rolling bearing, fault diagnosis

摘要：

滚动轴承早期故障信号具有能量小、频带分布宽等特征,易受到其他能量较大的振源信号的干扰,致使传统滤波降噪方法存在很大的局限性。针对这一特点,提出经验模式分解(EMD)和独立分量分析(ICA)相结合的联合降噪新方法。将单通道振动信号进行EMD分解,基于互相关准则对分解后的本征模函数进行重组,构造虚拟噪声通道,并以此作为ICA的输入矩阵,采用FastICA算法实现源信号和噪声信号的分离,从而达到降噪的目的。将该方法应用于滚动轴承故障诊断中,对降噪后的重构信号进行频谱分析,进而判断滚动轴承的运行状态。仿真和试验分析结果表明该方法有效可行。

关键词: 经验模式分解, 独立分量分析, 滚动轴承, 故障诊断

CLC Number:

ZHANG Dun-Gong, LI Lin-Ji, MA Wen-Peng, LI Zhou-Yu, LIU Yu. Application of EMD-ICA to Fault Diagnosis of Rolling Bearings[J]. China Mechanical Engineering, 2013, 24(11): 1468-1472.

张俊红, 李林洁, 马文朋, 李周裕, 刘昱. EMD-ICA联合降噪在滚动轴承故障诊断中的应用[J]. 中国机械工程, 2013, 24(11): 1468-1472.

References

[1]何田，刘献栋，李其汉．噪声背景下检测突变信息的奇异值分解技术[J]．振动工程学报，2006，19(3)：399-403．
He Tian，Liu Xiandong，Li Qihan．An Improved Method of Detecting Abrupt Information Based on Singularity Value Decomposition in Noise Background[J]．Journal of Vibration Engineering，2006，19(3)：399-403．
[2]胡爱军，唐贵基，安连锁．基于数学形态学的旋转机械振动信号降噪方法[J]．机械工程学报，2006，42(4)： 127-130．
Hu Aijun，Tang Guiji，An Liansuo．De-noising Technique for Vibration Signals of Rotating Machinery Based on Mathematical Morphology Filter[J]．Chinese Journal of Mechanical Engieering，2006，42(4)：127-130．
[3]Donoho D L．De-Noising by Soft-Thresholding[J]．IEEE Transactions on Information Theory，1995，41(3)：613-627．
[4]Donoho D L，Johnstone I M．Adapting to Unknown Smoothness via Wavelet Shrinkage[J].Journal of the American Statiscal Association ，1995，90(432)：1200-1224．
[5]Flandrin P，Rilling G， Goncalves P． Empirical Mode Decomposition as a Filter Bank[J]．IEEE Signal Processing Letters，2004，11(2)：112-114．
[6]杨建文，贾民平，许飞云，等．基于经验模式分解的旋转机械振动信号降噪处理[J]．中国工程科学，2005，7(8)：66-69．
Yang Jianwen，Jia Minping，Xu Feiyun ，et al．Noise Reduction of Vibration Signal of Cyclic Machine Based on the EMD[J]．Engineering Science，2005，7(8)：66-69．
[7]Boudraa A O，Cexus J C．EMD-Based Signal Filtering[J]．IEEE Instrumentation Transactions on Instrumentation and Measurement，2007，56(6)：2196-2202．
[8]季忠，金涛，杨炯明，等．基于独立分量分析的消噪方法在旋转机械特征提取中的应用[J]．中国机械工程，2005，16(1)：50-53．
Ji Zhong，Jin Tao，Yang Jiongming，et al．Applications of Noise-Reduction with ICA in Feature Extraction of Rotating Machinery[J].China Mechanical Engineering，2005，16(1)：50-53．
[9]焦卫东，杨世锡，吴昭同．基于独立分量分析的噪声消除技术研究[J]．浙江大学学报(工学版)，2004，38(7)：581-584．
Jiao Weidong，Yang Shixi，Wu Zhaotong．Study of Noise Removal Technique Based on Independent Component Analysis[J].Journal of Zhejiang University(Engineering Science)，2004，38(7)： 872-876．
[10]Peng Z K，Peter W T，Chu F L．A Comparison Study of Improved Hilbert-Huang Transform and Wavelet Transform：Application to Fault Diagnosis for Rolling Bearing[J].Mechanical Systems and Signal Processing，2005，19(5)：974-988．

[1]	LUO Ji-Sai, XU De-Ge, SHI Mei-Li. Gearbox Fault Diagnosis with Rotating Speed Fluctuation Based on SVD and Chirplet Path Pursuit Algorithm [J]. J4, 201016, 21(16): 1947-1951.
[2]	HU Chao, LIU Zuo-Min. Research on Rolling Bearing’s Heathcote’s Stick-slip Model Based on Materials Compatibility [J]. J4, 201016, 21(16): 1969-1973.
[3]	XIAO Gang, GU Hairui, DONG Jinjin, WANG Qibing, LU Jiawei. Simulation Data-driven Migration Diagnosis Method for Guide Rail Faults in Long-term Service Elevators [J]. China Mechanical Engineering, 2024, 35(01): 125-135.
[4]	YU Shubo, LIU Zhansheng, ZHAO Chen. Dynamics Simulation Data Driven Domain Adaptive Intelligent Fault Diagnosis [J]. China Mechanical Engineering, 2023, 34(23): 2832-2841.
[5]	CHEN Jian, XU Chang, XU Tingliang, . Centrifugal Pump Fault Diagnosis Methods Based on Dislocation Superposition Methods and Improved Probabilistic Neural Networks [J]. China Mechanical Engineering, 2023, 34(23): 2854-2861.
[6]	QIN Guohao, ZHANG Kai, DING Kun, HUANG Fengfei, ZHENG Qing, DING Guofu, . Dynamic Wide Convolutional Residual Network for Bearing Fault Diagnosis Method [J]. China Mechanical Engineering, 2023, 34(18): 2212-2221.
[7]	QIAN Mengui, CHEN Tao, YU Yaoxiang, GUO Liang, GAO Hongli, LI Weilin, . A Method for Constructing Bearing HIs with IGI Weighting [J]. China Mechanical Engineering, 2023, 34(15): 1813-1819，1855.
[8]	DONG Shaojiang, ZHOU Cunfang, CHEN Lili, XU Xiangyang. Cross-domain Fault Diagnosis of Bearings Based on Discriminant Feature Extraction and Dual-domain Alignment [J]. China Mechanical Engineering, 2023, 34(15): 1856-1863.
[9]	MENG Kang, TENG Wei, PENG Dikang, XIANG Ling, LIU Yibing. Operating Mechanism and Data Driven Approach for Fault Alarm of Wind Turbine Gearbox Systems [J]. China Mechanical Engineering, 2023, 34(12): 1476-1485.
[10]	ZHENG Jinde, CHEN Yan, TONG Jinyu, PAN Haiyang. RGCMvMRDE and Its Applications in Rolling Bearing Fault Diagnosis [J]. China Mechanical Engineering, 2023, 34(11): 1315-1325.
[11]	ZHANG Long, HU Yanqing, ZHAO Lijuan, ZHANG Hao. Multichannel Information Fusion and Deep Transfer Learning for Rotating Machinery Fault Diagnosis [J]. China Mechanical Engineering, 2023, 34(08): 966-975.
[12]	ZHANG Long, ZHANG Hao, WANG Chaobing, WANG Xiaobo, WEN Peitian, ZHAO Lijuan. Explicit Dynamics Driving Fault Diagnosis Method for Bearing Variable Conditions [J]. China Mechanical Engineering, 2023, 34(08): 982-992.
[13]	DONG Shaojiang, ZHU Peng, ZHU Sunke, LIU Lanhui, XING Bin, HU Xiaolin. Fault Diagnosis Method of Rolling Bearings Based on Simulation Data Drive and Domain Adaptation [J]. China Mechanical Engineering, 2023, 34(06): 694-702.
[14]	LI Dongyang, YUAN Dongfeng, ZHANG Haixia, ZHENG Anzhu, DI Zijun, LIANG Daojun, . Research on Intelligent Tool Fault Diagnosis System of Machine Tools with Cloud-Edge-Device Collaboration [J]. China Mechanical Engineering, 2023, 34(05): 584-594.
[15]	ZHAO Jing, YANG Shaopu, LI Qiang, LIU Yongqiang, . A New Transfer Learning Method with Residual Attention and Its Applications on Rolling Bearing Fault Diagnosis [J]. China Mechanical Engineering, 2023, 34(03): 332-343.