XIAO Shungen,SONG Mengmeng,KONG Qingguang,et al.A new fault diagnosis method of rolling bearing based on EEMD de-noising and undecimated lifting scheme packet[J].Journal of Yanbian University,2015,41(01):57-63.
基于EEMD降噪与非抽样提升小波包的滚动轴承故障诊断方法
- Title:
- A new fault diagnosis method of rolling bearing based on EEMD de-noising and undecimated lifting scheme packet
- Keywords:
- rolling bearing; EEMD; correlation coefficient-kurtosis criterion; frequency aliasing; undecimated lifting scheme packet
- 分类号:
- TH165+.3
- 文献标志码:
- A
- 摘要:
- 针对传统小波包在诊断滚动轴承隐含故障中存在频率混叠、精度不高等问题,提出一种基于集成经验模态分解(ensemble empirical mode decomposition,EEMD)降噪与非抽样提升小波包相融合的故障诊断方法.首先利用EEMD方法分解原始故障信号得到多个本征模态函数(intrinsic mode function,IMF)分量,然后计算各个IMF分量与原始信号间的相关系数,并与设置的相关系数阈值相比较,将小于阈值的IMF分量视为伪分量予以剔除; 对剩余的IMF分量采用峭度准则再次筛选最优IMF分量进行重构,进而实现降噪目的.为了避免传统小波包因采取抽样运算方式导致频率混叠情况,文中采用非抽样运算的提升小波包来分解降噪信号,并采用Hilbert变换进行包络解调分析得到滚动轴承的故障位置.仿真实验和滚动轴承内圈故障应用实例表明:采用EEMD分解原始故障信号,结合相关系数-峭度准则,达到了很好的降噪效果; 采用非抽样提升小波包比传统小波包具有更高的故障诊断精度,且不存在频率混叠问题.
- Abstract:
- Traditional wavelet packet in the implied fault diagnosis of rolling bearing exists some problems, such as frequency aliasing, the accuracy is not high, and so on. We propose a fault diagnosis method based on ensemble empirical mode decomposition(EEMD)de-noising and undecimated lifting scheme packet. Using EEMD method to decompose the original signals to obtain a lot of intrinsic mode function(IMF)components, calculated the correlation coefficients between each IMF component and the original signals, and compared with the threshold of correlation coefficients, if the correlation coefficients of IMF were less than the threshold, it would be deemed spurious IMF components and abandoned. The remaining IMF components were used kurtosis criterion to screen the optimal IMF components to reconstruct again, thus achieving the purpose of de-noising. In order to avoid the traditional wavelet packet produced frequency aliasing due to decimated operation, we used undecimated lifting wavelet packet to decompose de-noising signals, and de-noising signals were demodulated with Hilbert transform to get rolling bearing fault location. The simulation experiment and the application examples of rolling bearing inner fault show that: using EEMD to decompose, combining correlation coefficient-kurtosis criterion, attains good de-noising; the undecimated lifting wavelet packet has higher fault diagnosis accuracy than the traditional wavelet packet, and do not exist the problem of frequency aliasing.
参考文献/References:
[1] 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.
[2] 边杰,王平,梅庆.EEMD结合能量特征和小波降噪的轴承故障诊断[J].广西大学学报:自然科学版,2014,39(6):1206-1211.
[3] 肖顺根,宋萌萌.基于小波包能量神经网络的滚动轴承故障诊断方法[J].机械强度,2014,36(3):340-346.
[4] 韩星,熊静琪,王李立,等.基于小波去噪和最小二乘支持向量机的滚动轴承故障诊断研究[J].机床与液压,2014,42(9):155-158.
[5] Sweldens W. The lifting scheme: a construction of second generation wavelet[J]. SIAM Journal on Mathematics Analysis, 1997,29(2):511-546.
[6] 陈换过,江金寿,李剑敏,等.基于提升小波包和神经网络的结构损伤检测[J].振动、测试与诊断,2013,33(1):116-121.
[7] 谭晓东,覃德泽.提升小波包和改进BP神经网络相融合的新故障诊断算法[J].计算机测量与控制,2014,22(8):2405-2408.
[8] 段晨东,郭研.基于提升小波包变换的滚动轴承包络分析诊断方法[J].农业机械学报,2008,39(5):192-196.
[9] 张超,陈建军.EEMD方法和EMD方法抗模态混叠对比研究[J].振动与冲击,2010,29(S):87-90.
[10] 李昌林,孔凡让,黄伟国,等.基于EEMD和Laplace小波的滚动轴承故障诊断[J].振动与冲击,2014,33(3):63-69.
[11] 陈仁祥,汤宝平,吕中亮.基于相关系数的EEMD转子振动信号降噪方法[J].振动、测试与诊断,2012,32(4):542-546.
[12] 陈敬龙,张来斌,段礼祥,等.基于提升小波包的往复压缩机活塞-缸套磨损故障诊断[J].中国石油大学学报:自然科学版,2011,35(1):130-134.
[13] Jiang Hongkai, He Zhengjia, Duan Chendong. Gearbox fault diagnosis using adaptive redundant lifting scheme[J]. Mechanical Systems and Signal Processing, 2006,20(8):1992-2006.
备注/Memo
收稿日期: 2015-01-11 作者简介: 肖顺根(1983—),男,讲师,研究方向为人工智能和机械设备故障诊断.基金项目: 福建省教育厅A类科技项目(JA14332); 宁德师范学院“服务宁德区域经济和产业发展”专项课题(2013F25,2013F26); 福建省自然科学基金资助项目(2015J01643)