摘要为了提取受强背景噪声干扰的信号中的弱故障特征，提出一种基于局部均值分解的多层混合滤噪方法(Local mean decomposition- Multilayer hybrid de-noising, LMD-MHD)。针对LMD分解所得的乘积函数(Product function，PF)分量可能存在虚假分量的问题，提出一种多指标综合决策方法，结合各指标在不同故障阶段的量化能力，筛选出合理的有效PF分量。将小波阈值滤噪设为奇异值分解(Singular value decomposition，SVD)的前置处理单元，使保留的较大奇异值以特征信息贡献为主，减少干扰成分，并采用信号快速傅里叶变换结果中主频率个数来确定奇异值重构阶数。轴承早期故障振动信号的试验结果表明，该方法能够可有效滤除随机噪声和脉冲干扰，提取强背景噪声下的早期弱故障特征，提高轴承故障诊断的准确性。
Abstract：In order to extract fault features embedded in weak signals consisting of much noise, a new method named local mean decompositionmultilayer hybrid de-noising (LMD-MHD) was proposed. A series of product functions (PFs) were obtained after LMD. Multiple criteria decision was proposed to select the effective PF components reasonably, which combined quantitative ability of each index at different fault stages. Then, the wavelet threshold de-noising was used as the pre-filter process. The order of effective ranks was determined by the number of main frequency in the Fast Fourier transformation result of the signal. Experimental results on the actual bearing vibration signals demonstrate that this method can effectively remove interference of noise and extract the faint fault features. Thus, the proposed method can be used to improve the accuracy of bearing fault diagnosis.
吕靖香 余建波. 基于多层混合滤噪的轴承早期弱故障特征提取方法[J]. 振动与冲击, 2018, 37(8): 22-27.
LVJingxiang, YU Jianbo. Early fault diagnosis of bearings based on multilayer hybrid de-noising. JOURNAL OF VIBRATION AND SHOCK, 2018, 37(8): 22-27.
[1] Yu J. Local and nonlocal preserving projection for bearing defect classification and performance assessment [J]. IEEE Transactions on Industrial Electronics, 2012, 59(5): 2363-2376.
[2] 王衍学, 何正嘉, 訾艳阳, 等. 基于LMD的时频分析方法及其机械故障诊断应用研究[J].振动与冲击, 2012, 31(9): 9-12.
WANG Yan-xue, HE Zheng-jia, ZI Yan-yang, et al. Several key issues of local mean decomposition method used in mechanical fault diagnosis [J]. Journal of Vibration and Shock, 2012, 31(9): 9-12.
[3] Smith J S. The local mean decomposition and its application to EEG perception data [J]. Journal of the Royal Society Interface, 2005, 2(5): 443-454.
[4] Song R, Ma M, Xie C. Application of vibration signal analysis method based on wavelet and LMD [J].Journal of Information & Computational Science, 2015, 12(4): 1503-1510.
[5] 孙伟, 熊邦书, 黄建萍, 等. 小波包降噪与LMD相结合的滚动轴承故障诊断方法[J].振动与冲击, 2012, 31(18): 153-156.
SUN Wei, XIONG Bang-shu, HUANG Jian-ping, et al. Fault diagnosis of a rolling bearing using wavelet packet de-noising and LMD [J]. Journal of Vibration and Shock, 2012, 31(18): 153-156.
[6] Liu Z, He Z, Guo W, et al. A hybrid fault diagnosis method based on second generation wavelet de-noising and local mean decomposition for rotating machinery [J]. ISA transactions, 2016, 61: 211-220.
[7] 王建国，李建, 万旭东. 基于奇异值分解和局域均值分解的滚动轴承特征提取方法[J].机械工程学报，2015, 51(3): 104-110.
WANG Jian-guo, LI Jian, WAN Xu-dong. Fault feature extraction method of rolling bearings based on singular value decomposition and local mean decomposition [J]. Journal of Mechanical Engineering, 2015, 51 (3): 104-110.
[8] 马朝永, 刘茜, 段建民. 基于LMD与奇异值差分谱的滚动轴承故障诊断方法[J].北京工业大学学报,2014, 40(2):182-188.
MA Chao-yong, LIU Qian, DUAN Jian-min. Fault diagnosis method of rolling bearings based on LMD and singular value difference spectrum [J].Journal of Beijing University of Technology, 2014, 40(2): 182-188.
[9] 聂振国, 赵学智. PCA与SVD信号处理效果相似性与机理分析[J].振动与冲击, 2016, 35(2): 12-17.
NIE Zhen-guo, ZHAO Xue-zhi. Similarity of signal processing effect between PCA and SVD and its mechanism analysis [J]. Journal of Vibration and Shock, 2016, 35(2): 12-17.
[10] Sun J, Xiao Q, Wen J, et al. Natural gas leak location with K–L divergence-based adaptive selection of Ensemble Local Mean Decomposition components and high-order ambiguity function [J]. Journal of Sound and Vibration, 2015, 347: 232-245.
[11] 杨梅, 陈思汉, 吴昊, 等. LMD滤噪算法及在旋转机械转子故障诊断中的应用[J].噪声与振动控制, 2015, 35(2): 160-164.
YANG Mei, CHEN Si-han, WUHao, et al. An improved LMD-based de-noising algorithm for fault diagnosis of rotating machinery [J]. Noise and Vibration Control, 2015, 35(2): 160-164.
[12] 唐贵基, 王晓龙. 基于局部均值分解和切片双谱的滚动轴承故障诊断研究[J].振动与冲击, 2013, 32(24): 83-88.
TANGGui-Ji, WANG Xiao-long. Fault diagnosis of roller bearings based on local mean decomposition and slice bispectrum [J]. Journal of Vibration and Shock, 2013, 32(24): 83-88.
[13] 鞠萍华, 秦树人, 赵玲. 基于LMD的能量算子解调方法及其在故障特征信号提取中的应用[J].振动与冲击, 2011, 30(2): 1-4.
JU Ping-hua, QIN Shu-ren, ZHAO-Ling. Energy operator demodulating approach based on LMD and its application in extracting characteristics of a fault signal [J]. Journal of Vibration and Shock, 2011, 30(2): 1-4.
[14] Yu J. Bearing performance degradation assessment using locality preserving projections [J]. Expert Systems with Applications, 2011, 38(6): 7440-7450.
[15] 曾鸣，杨宇，郑近德,等. u-SVD降噪算法及其在齿轮故障诊断中的应用[J].机械工程学报，2015，51(3)：95-103.
ZENG Ming, YANG Yu, ZHENG Jin-de, et al. μ-SVD based de-noising method and its application to gear fault diagnosis [J]. Journal of Mechanical Engineering, 2015, 51 (3): 95-103.
[16] 徐峰，刘云飞，宋军. 基于中值滤波-SVD和EMD的声发射信号特征提取[J].仪器仪表学报，2011，32(12)：2712-2719.
XU Feng, LIU Yun-fei, SONG Jun. Feature extraction of acoustic emission signals based on median filter-singular value decomposition and empirical mode decomposition [J]. Chinese Journal of Scientific Instrument, 2011, 32(12): 2712-2719.
[17] 钱征文，程礼，李应红. 利用奇异值分解的信号降噪方法[J].振动、测试与诊断, 2011，31(4)：459-463.
QIAN Zheng-wen, CHENG Li, LI Ying-hong. Noise reduction method based on singular value decomposition [J]. Journal of Vibration, Measurement & Diagnosis, 2011, 31 (4): 459-463.
[18] Case western reserve university bearing data center website [EB/OL].http//csegroups.case.edu/bearingdatacenter/pages/download-data-file, 2011-05-11.
[19] 赵学智, 叶邦彦, 陈统坚. 基于小波—奇异值分解差分谱的弱故障特征提取方法[J].机械工程学报, 2012, 48(7): 37-48.
ZHAO Xue-zhi, YE Bang-yan, CHEN Tong-jian. Extraction method of faint fault feature based on wavelet-SVD difference spectrum [J]. Journal of Mechanical Engineering, 2012, 48(7): 37-48.
[20] 邵忍平, 曹精明, 李永龙. 基于EMD小波阈值去噪和时频分析的齿轮故障模式识别与诊断[J].振动与冲击，2012, 31(8): 96-101.
SHAO Ren-ping, CAO Jing-ming, LI Yong-long. Gear fault pattern identification and diagnosis using time-frequency analysis and wavelet threshold de-noising based on EMD [J]. Journal of Vibration and Shock, 2012, 31(8): 96-101.
[21] Han T, Jiang D, Wang N. The Fault Feature Extraction of Rolling Bearing Based on EMD and Difference Spectrum of Singular Value [J]. Shock and Vibration, 2016, 2016: 1-14.
