基于信源估计和频域反卷积的滚动轴承故障特征分离与辨识

中国机械工程

基于信源估计和频域反卷积的滚动轴承故障特征分离与辨识

张建宇1;孟浩2;胥永刚3

1. 北京工业大学先进制造技术北京市重点实验室，北京，100124
2.卧龙电气章丘海尔电机有限公司,章丘，250200
3. 北京市精密测控技术与仪器工程技术研究中心，北京，100124

出版日期:2017-01-10 发布日期:2017-01-04
基金资助:
北京市教委科技计划资助项目(KM201410005027)

Fault Separation and Recognition of Rolling Bearings Based on Source Number Estimation and FDBD

ZHANG Jianyu1;MENG Hao2;XU Yonggang3

1.Key Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Beijing, 100124
2.Zhangqiu Haier Motor Co. Ltd., Wolong Electrics, Zhangqiu, Shandong，250200
3. Beijing Engineering Research Center of Precision Measurement Technology and Instrument, Beijing, 100124

Online:2017-01-10 Published:2017-01-04

摘要/Abstract

摘要： 针对轴承故障的振动特征由于受到强振源的抑制作用而增加了故障分离与辨识难度的问题，建立了基于信源估计和频域反卷积的故障诊断方法。利用小波包分解将信号分离成多个子带信号，并和奇异值分解相结合，解决欠定条件下的信号源数估计问题；根据估计的源数，选取相应维数的观测信号，通过短时傅里叶变换、复数域独立分量分析、相关排序、短时傅里叶逆变换，完成频域反卷积的分析过程，实现故障特征的分离与提取。仿真信号和实验数据均验证了该方法在故障特征分离与微弱特征辨识中的有效性。

关键词: 小波包分解, 奇异值分解, 短时傅里叶变换, 复数域独立分量分析, 频域反卷积

Abstract: According to the problems that the vibration features of bearing faults were hard to separate and recognize in strong vibration source inhibition, a diagnosis method was established based on source number estimation and FDBD algorithm. Wavelet packet decomposition was used to divide the signals into multiple sub band signals, and SVD was selected to estimate the signal source numbers in underdetermined conditions. The multiple dimension signals were constructed based on the source number estimation. The FDBD algorithm, which included STFT, fastICA in complex domain, relevance ranking and inverse STFT, was finally applied on fault feature separation and extraction. The effectiveness of the method was validated in fault feature separation and weak feature recognition by the simulation signals and experimental data of rolling bearing faults.

Key words: wavelet packet decomposition, singular value decomposition(SVD), short time Fourier transform (STFT), fastICA in complex domain, frequency domain blind deconvolution (FDBD)

中图分类号:

TH16

张建宇, 孟浩, 胥永刚. 基于信源估计和频域反卷积的滚动轴承故障特征分离与辨识[J]. 中国机械工程.

ZHANG Jianyu, MENG Hao, XU Yonggang. Fault Separation and Recognition of Rolling Bearings Based on Source Number Estimation and FDBD[J]. China Mechanical Engineering.

参考文献

[1]HYVRINEN A, OJA E. A Fast Fixed-point Algorithm for Independent Component Analysis[J]. Neural Computation, 1997, 9(7):1483-1492.
[2]YPMA A, PAJUNEN P. Rotating Machine Vibration Analysis with Second-order Independent Compon-ent Analysis[C]// Proceedings of the First International Workshop on Independent Component Analysis and Signal Separation Ica. Aussois,1998:37-42.
[3]PYHNEN S, JOVER P. Independent Component Analysis of Vibrations for Fault Diagnosis of an Induction Motor[J]. Circuits, Signals and Systems,2003,1:203-208
[4]张海军, 屈梁生. 一种提高诊断信息质量的方法[J]. 西安交通大学学报，2002, 13(2):295-299.
ZHANG Haijun, QU Liangsheng. Method to Improve the Quality of Diagnostic Information[J]. Journal of Xi'an Jiaotong University, 2002,13(2):295-299.
[5]TORKKOLA K. Blind Separation of Delayed Sources Based on Information Maximization[C]// IEEE Proceedings of International Conference on Acoustics, Speech, and Signal Processing. Atlanta, 1996:3510-3513.
[6]SERVIERE C, FABRY P. Blind Source Separation of Noisy Harmonic Signals for Rotating Machine Diagnosis[J]. Journal of Sound and Vibration, 2004, 272:317-339.
[7]PELED R, BRAUN S, Zacksenhouse M. A Blind Deconvolution Separation of Multiple Sources, with Application to Bearing Diagnostics[J]. Mechanical Systems and Signal Processing, 2005, 19:1181-1195.
[8]刘婷婷, 任兴民, 杨永锋,等. 盲解卷积的机械振动信号分离技术[J]. 振动、测试与诊断，2009，29(4):419-423.
LIU Tingting, REN Xingmin, YANG Yongfeng, et al. The Technology of Mechanical Vibration Signals Separation Based on Blind Deconvolution[J].Journal of Vibration, Measurement and Diagnosis,2009,29(4):419-423.
[9]叶红仙, 杨世锡, 杨将新. 多振源卷积混合的时域盲源分离算法[J]. 机械工程学报，2009, 45(1):190-199.
YE Hongxian, YANG Shixi, YANG Jiangxin. Temporal Blind Source Separation Algorithm for Convolution Mixtures with Muti Vibration Sources[J]. Chinese Journal of Mechanical Engineering, 2009, 45(1):190-199.
[10]潘楠, 伍星, 迟毅林, 等. 基于频域盲解卷积的机械设备状态监测与故障诊断[J]. 振动与冲击,2012, 31(12):34-41.
PAN Nan, WU Xing, CHI Yilin, et al. Mechanical Equipment Condition Monitoring and Fault Diagnosis Based on Frequency-domain Blind Deconvolution[J]. Journal of Vibration and Shock, 2012, 31(12):34-41.

[1]	唐贵基;李楠楠;王晓龙. 综合改进奇异谱分解和奇异值分解的齿轮故障特征提取方法[J]. 中国机械工程, 2020, 31(24): 2988-2996.
[2]	孟宗;刘子涵;吕蒙. 基于改进奇异值分解滤波和谱峭度的滚动轴承故障诊断[J]. 中国机械工程, 2020, 31(20): 2420-2428.
[3]	张文颢1;李永健2,3;张卫华1. 基于K-奇异值分解和层次化分块正交匹配算法的滚动轴承故障诊断[J]. 中国机械工程, 2019, 30(04): 406-412.
[4]	王友仁;王俊;黄海安. 基于非线性短时傅里叶变换阶次跟踪的变速行星齿轮箱故障诊断[J]. 中国机械工程, 2018, 29(14): 1688-1695.
[5]	张宇飞, 么子云, 唐松林, 朱丽娜, 张进杰. 一种基于主成分分析和支持向量机的发动机故障诊断方法[J]. 中国机械工程, 2016, 27(24): 3307-3311.
[6]	吴芬, 黄美发, 陈磊磊, 吴常林. 基于多分辨率奇异值分解的表面滤波方法[J]. 中国机械工程, 2016, 27(13): 1766-1772.
[7]	何志坚, 周志雄. 基于FSWT细化时频谱SVD降噪的冲击特征分离方法[J]. 中国机械工程, 2016, 27(09): 1184-1190.
[8]	毋文峰, 李浩, 朱露. 基于奇异值融合的机械设备盲信息提取[J]. 中国机械工程, 2015, 26(22): 3028-3033.
[9]	李拯, 吴维亮, 屈文忠, 肖黎. 基于非线性超声调制的疲劳裂纹识别方法[J]. 中国机械工程, 2015, 26(17): 2384-2388.
[10]	赵志宏1, 2, 杨绍普2, 李韶华2. 基于Hilbert谱奇异值的轴承故障诊断[J]. 中国机械工程, 2013, 24(3): 346-350.
[11]	贺尚红, 赵天林, 石芳. 输入输出测量噪声干扰下连续Hammerstein模型参数直接辨识 [J]. 中国机械工程, 2012, 23(4): 458-463.
[12]	陈书涵, 严宏志. 基于刀倾法的螺旋锥齿轮齿面误差修正算法研究 [J]. 中国机械工程, 2011, 22(9): 1080-1084.
[13]	丁建明, 林建辉, 尹燕莉, 杨强. 轴承故障动态检测的迭代交叉信息熵方法 [J]. 中国机械工程, 2010, 21(9): 1058-1061.
[14]	钱苏翔, 杨世锡, 焦卫东, 胡红生. 基于独立分量分析与小波包分解的混叠声源信号波形恢复 [J]. 中国机械工程, 2010, 21(24): 2956-2961.
[15]	吴常坤, 赵丽萍. 基于小波分析和SVM的控制图模式识别 [J]. 中国机械工程, 2010, 21(13): 1572-1576.