混合插值的ESMD在电机轴承故障特征提取的应用

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振动与冲击 ›› 2020, Vol. 39 ›› Issue (4) : 266-272.

论文

宿文才1，张树团1，刘涛1，井超2

作者信息 +

Application of hybrid interpolated ESMD in motor bearing fault feature extraction

SU Wencai1 , ZHANG Shutuan1 , LIU Tao1 , JING Chao2

Author information +

文章历史 +

摘要

针对极点对称模态分解（ESMD）处理电机轴承故障信号存在局部模态混叠的问题，提出了一种基于有理Hermite插值和三次样条插值改进的ESMD电机轴承故障特征提取方法。由于首先有理Hermite插值可通过控制形状参数来调节插值曲线，将固有模态分量(IMF)的瞬时频率带宽作为的优化准则，采用有理Hermite插值与三次样条插值相结合，既避免了插值耗时过长，又考虑了插值曲线的平滑性；采用自适应权重调整的粒子群算法（PSO）确定每阶IMF的最优形状控制参数，避免陷入局部最优，使得IMF最优，从而提高ESMD自适应性和分解精度。试验结果表明，该方法可有效提取电机轴承故障特征，并有效缓解ESMD模态混叠，与其它方法相比分解效果更好。

Abstract

Aiming at the problem of local modal aliasing of motor bearing fault signal for extreme-point symmetric mode decomposition (ESMD), an improved fault condition of ESMD motor bearings based on rational Hermite interpolation and cubic spline interpolation was proposed.Since the first rational Hermite interpolation can adjust the interpolation curve by controlling the shape parameters, the instantaneous frequency bandwidth of the Intrinsic Mode Function (IMF) was used as the optimization criterion, and the combination of rational Hermite interpolation and cubic spline interpolation was avoided.The interpolation takes too long, and the smoothness of the interpolation curve is considered.The particle swarm optimization (PSO) algorithm with adaptive weight adjustment determines the optimal shape control parameters of each order IMF, avoiding falling into local optimum and making the IMF optimal, thereby improving ESMD adaptability and decomposition accuracy.The experimental results show that the proposed method can effectively extract the fault characteristics of motor bearings and effectively alleviate the aliasing of ESMD modes.Compared with other methods, the decomposition effect is better.
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导出引用

宿文才1，张树团1，刘涛1，井超2. 混合插值的ESMD在电机轴承故障特征提取的应用[J]. 振动与冲击, 2020, 39(4): 266-272

SU Wencai1,ZHANG Shutuan1,LIU Tao1,JING Chao2 . Application of hybrid interpolated ESMD in motor bearing fault feature extraction[J]. Journal of Vibration and Shock, 2020, 39(4): 266-272

参考文献

[1] JINLIANG WANG, ZONGJUN LI. EXTREME-POINT SYMMETRIC MODE DECOMPOSITION METHOD FOR DATA ANALYSIS[J]. Advances in Adaptive Data Analysis, 2013, 05(3):1137-1137.
[2] 王金良, 李宗军. 极点对称模态分解方法:数据分析与科学探索的新途径[M]. 高等教育出版社, 2015.
Wang Jinliang, Li Zongjun. Symmetric Mode Decomposition of Pole: A New Approach to Data Analysis and Scientific Exploration[M]. Higher Education Press, 2015.
[3] 李伟红, 田真真, 龚卫国,等. 改进的ESMD用于公共场所异常声音特征提取[J]. 仪器仪表学报, 2016, 37 (11) : 2429-2437.
Li Weihong, Tian Zhenzhen, Gong Weiguo, et al. Improved ESMD for the extraction of abnormal sound features in public places [J]. Chinese Journal of Scientific Instrument, 2016, 37 (11): 2429-2437.
[4] 张淑清, 徐剑涛, 姜安琦,等. 基于极点对称模态分解和概率神经网络的轴承故障诊断[J]. 中国机械工程, 2017, 28(4):425-431.
Zhang Shuqing, Xu Jiantao, Jiang Anqi, et al. Bearing Fault Diagnosis Based on Pole Symmetric Mode Decomposition and Probabilistic Neural Network[J]. China Mechanical Engineering, 2017, 28(4): 425-431.
[5] 叶卫东, 杨涛. 融合极点对称模态分解与时频分析的单通道振动信号盲分离方法[J]. 计算机应用, 2016, 36(10):2933-2939.
YE Weidong, YANG Tao. Blind separation method for single-channel vibration signals based on polar point symmetric modal decomposition and time-frequency analysis[J]. Journal of Computer Applications, 2016, 36(10): 2933-2939.
[6] 时培明,王敬,温江涛, 等.基于包络线算法改进EMD的旋转机械故障诊断方法研究[J].计量学报,2016,(1):62-66.
Shi Peiming, Wang Jing, Wen Jiangtao, et al. Research on fault diagnosis method of rotating machinery based on envelope algorithm for improving EMD[J].Acta Metrologica Sinica, 2016, (1): 62-66.
[7] 李军成, 刘纯英, 杨炼. 带参数的四次Hermite插值样条[J]. 计算机应用, 2012, 32(7):1868-1870.
LI Jun-cheng, LIU Chun-ying, YANG Lian. Four-time Hermite interpolation spline with parameters[J]. Journal of Computer Applications, 2012, 32(7): 1868-1870.
[8] 赵海洋, 徐敏强, 王金东. 有理Hermite插值局部均值分解方法及其往复压缩机故障诊断应用[J]. 机械工程学报, 2015, 51(1):83-89.
Zhao Haiyang, Xu Minqiang, Wang Jindong. Rational Hermite Interpolation Local Mean Decomposition Method and Its Application to Fault Diagnosis of Reciprocating Compressors[J]. Journal of Mechanical Engineering, 2015, 51(1): 83-89.
[9] 郭泰, 邓忠民, 徐萌. 基于粒子群优化的改进EMD算法在轴承故障特征提取中的应用[J]. 振动与冲击, 2017, 36(16): 182-187.
Guo Tai, Deng Zhongmin, Xu Meng. Application of Improved EMD Algorithm Based on Particle Swarm Optimization in Bearing Fault Feature Extraction[J]. JOURNAL OF VIBRATION AND SHOCK, 2017, 36(16): 182-187.
[10] 韩江，谷涛涛，夏链，董方方. 基于混合插值的工业机器人关节轨迹规划算法[J]. 中国机械工程, 2018, 29(12): 1460-1466.
HAN Jiang; GU Taotao; Xia Chain; Dong Fangfang. Joint trajectory planning algorithm for industrial robot based on hybrid interpolation[J]. China Mechanical Engineering, 2018, 29(12): 1460-1466.
[11] 吕志峰, 张金生, 王仕成,等. 基于自适应权重PSO算法的磁屏蔽装置优化设计[J]. 中国惯性技术学报, 2017, 25(6):799-803
Lü Zhifeng, Zhang Jinsheng, Wang Shicheng, et al. Optimization design of magnetic shielding device based on adaptive weight PSO algorithm[J]. Chinese Journal of Inertial Technology, 2017,25(6):799-803
[12] 薛晶晶, 何锋, 赵仕俊. 基于自适应惯性权重的混沌粒子群优化无线传感器网络成簇算法[J]. 计算机系统应用, 2017, 26(11):139-144.
XUE Jingjing, HE Feng, ZHAO Shijun. Clustering algorithm for wireless sensor networks based on chaotic particle swarm optimization based on adaptive inertia weight[J]. Journal of Computer Systems, 2017, 26(11): 139-144.
[13] 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(4):638-648.
Tang Guiji, Wang Xiaolong. Variational mode decomposition method and its application in early fault diagnosis of rolling bearings[J]. Journal of Vibration Engineering, 2016, 29(4): 638-648.
[14] 杨小强, 李沛, 黄杰,等. 基于极值波延拓的端点效应处理方法[J]. 系统工程与电子技术, 2016, 38(9):1987-1992.
Yang Xiaoqiang, Li Pei, Huang Jie, et al. Endpoint effect processing method based on extreme value wave extension[J]. Systems Engineering and Electronics, 2016, 38(9): 1987-1992.
[15] 马增强，张俊甲，王梦奇等，基于改进 EEMD 和谱峭度的滚动轴承故障诊断[J],图学学报，2017，38(5):663-669.
Ma Zeng, Zhang Junjia, Wang Mengqi et al. Fault diagnosis of rolling bearings based on improved EEMD and spectral kurtosis[J], Journal of Graphics, 2017, 38(5): 663-669.
[16] Case Western Reserve University. Bearing Data [EB/OL]．
http://csegroups.case.edu/bearingdatacenter/pages/download-data-file
[17] Smith W A, Randall R B. Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study[J]. Mechanical Systems & Signal Processing, 2015, 64-65:100-131.
[18] 张西宁,向宙,唐春华.一种深度卷积自编码网络及其在滚动轴承故障诊断中的应用[J].西安交通大学报,2018,52(07):1-8.
ZHANG Xining,XIANG Zhou,TANG Chunhua.A Deep Convolutional AutoEncoding Neural Network and Its Application in Bearing Fault Diagnosis[J].Journal of Xi'an Jiaotong University,2018,52(07):1-8.