机械振动信号自适应多尺度非线性动力学特征提取方法研究

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振动与冲击 ›› 2020, Vol. 39 ›› Issue (14) : 224-232.

论文

刘敏1，范红波1，张英堂1，李志宁1，杨望灿2

作者信息 +

Adaptive multi-scale method for the non-linear dynamic feature extraction of mechanical vibration signals

LIU Min1， FAN Hongbo1, ZHANG Yingtang1， LI Zhining1， YANG Wangcan2

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文章历史 +

摘要

针对机械振动信号的故障特征提取问题，提出了基于独立变分模态分解与多尺度非线性动力学参数的特征提取方法。①提出频谱循环相干系数选取匹配波形对机械振动信号进行端点延拓后再进行VMD分解得到不同频率尺度的IMF分量；②根据互相关准则选取有效的IMF分量进行核独立成分分析，分离出相互独立的有效故障特征频带分量；③计算各独立分量的复合多尺度模糊熵偏均值，并利用正交变换将独立分量正交化后构造多维超体，进而利用多维超体体积定义并计算信号的双测度分形维数，从而获得多尺度非线性动力学特征参数，实现机械故障诊断。仿真和实验结果表明：所提方法可有效抑制VMD分解的端点效应和模态混叠，信号分解效果好，特征参数分类精度高，极大地提高了机械故障诊断准确率。

Abstract

Aiming at the fault feature extraction of mechanical vibration signals, a feature extraction method based on the independent variational mode decomposition(VMD) and multi-scale nonlinear dynamic parameters was put forward.The spectral cyclic coherence coefficient was proposed to select the matching waveform which was used to complete the endpoint extension for the mechanical vibration signal.The extended signal was decomposed into some intrinsic mode functions (IMFs) in different frequency scales by using the VMD.The effective IMFs were selected according to the cross-correlation criterion and the independent components with effective frequency band were separated from the effective IMFs by using the kernel independent component analysis.The composite multi-scale fuzzy entropy partial mean of each independent IMF was calculated.The orthogonal transform was used to orthogonalize independent IMFs to construct a multi-dimensional hyperbody, and its volume was used to define and calculate the dual measure fractal dimension of the vibration signal.Thereby,the multi-scale nonlinear dynamic parameters were obtained to achieve mechanical fault diagnosis.The simulation and experimental results show that the proposed method can effectively suppress the end effect and mode mixing in the VMD,which improves the effect of signal decomposition; the feature parameters have higher classification accuracy, which greatly improves the accuracy of mechanical fault diagnosis.

导出引用

刘敏1，范红波1，张英堂1，李志宁1，杨望灿2. 机械振动信号自适应多尺度非线性动力学特征提取方法研究[J]. 振动与冲击, 2020, 39(14): 224-232

LIU Min1， FAN Hongbo1, ZHANG Yingtang1， LI Zhining1， YANG Wangcan2. Adaptive multi-scale method for the non-linear dynamic feature extraction of mechanical vibration signals[J]. Journal of Vibration and Shock, 2020, 39(14): 224-232

参考文献

[1] 沈微，陶新民，高珊，等. 基于同步挤压小波变换的振动信号自适应降噪方法[J]. 振动与冲击，2018，37(14):239-247.
SHEN Wei, TAO Xinmin, GAO Shan, et al. Self-adaptivede-noising algorithm for vibration signals based onsynchrosqueezed wavelet transforms [J]. Journal of Vibration and Shock, 2018,37(14):239-247.
[2] HamedAzami, Alberto Fernández, Javier Escudero, Refined multiscale fuzzy entropy based on standard deviation for biomedical signal analysis, Physica A: Statistical Mechanics and its Applications, 2017,46: 5261-276.
[3] 杨望灿，张培林，王怀光，等. 基于 EEMD 的多尺度模糊熵的齿轮故障诊断[J]. 振动与冲击，2015，34(14):163-167.
YANG Wangcan, ZHANG Peilin, WANG Huaiguang, et al. Gear fault diagnosis based on multiscale fuzzy entropy of EEMD[J]. Journal of Vibration and Shock, 2015,34(14):163-167.
[4]ChenX H,Cheng G, LiH, Y, et al.Diagnosing planetary gear faults using the fuzzy entropy of LMD and ANFIS, Journal of Mechanical Science and Technology, 2016,30 (6): 2453-2462.
[5]郑近德，潘海洋，程军圣，等. 基于复合多尺度模糊熵的滚动轴承故障诊断方法[J]. 振动与冲击，2016，35(8):116-123.
ZHENG Jinde, PAN Haiyang, CHENG Junsheng, et al. Composite multi-scale fuzzy entropy based rolling bearing fault diagnosis method [J]. Journal of Vibration and Shock, 2016, 35(8): 116-123.
[6] Konstantin D, Dominique Z. Variational mode decomposition
[J]. IEEE Transactions on Signal Processing, 2014, 62(3): 531-544.
[7] Zheng J D, Pan H Y, Cheng J S. Rolling bearing fault detection and diagnosis based on composite multiscale fuzzy entropy and ensemble support vector machines[J]. Mechanical Systems and Signal Processing, 2017, 85: 746-759.
[8] XuJ F, Jian Z Y, Lian X.An application of box counting method for measuring phase fraction, Measurement 100 (2017) 297–300.
[9] Wang B, HuX, LiH. Rolling bearing performance degradation condition recognition based on mathematical morphological fractal dimension and fuzzy C-means[J]. Measurement, 2017, 109: 1-8.
[10] ZhengZ, JiangW, WangZ, et al. Gear fault diagnosis method based on local mean decomposition and generalized morphological fractal dimensions[J]. Mechanism and Machine Theory, 2015, 91 (1): 151-167.
[11]张淑清，邢婷婷，何红梅，等. 基于VMD及广义分形维数矩阵的滚动轴承故障诊断[J]. 计量学报，2017，38(4):439-443.
ZHANG Shuqing, XING Tingting, HE Hongmei, et al. BearingFaultDiagnosisMethodBasedonVMD and GeneralizedFractalDimensionMatrix[J]. ActaMetrolo-
-gicaSinica,2017, 38(4):439-443.
[12]吴琛，项洪，杜喜朋. 基于数据/极值联合对称延拓的端点效应处理及其应用[J]. 振动与冲击，2017，36(22):178-184.
WU Chen, XIANG Hong, DU Xipeng. A process method for end effects of HHT based on data/extrema symmetrical extension and its application[J], Journal of Vibration and Shock,2017,36(22):178-184.
[13] 姜久亮，刘文艺，侯玉洁，等. 基于内积延拓 LMD 及 SVM 的轴承故障诊断方法研究[J]. 振动与冲击，2016，35(6): 104-108.
JIANG Jiuliang，LIU Wenyi，HOU Yujie, et al. Bearing fault diagnosis based on integral waveform extension LMD and SVM [J], Journal of Vibration and Shock, 2016, 35(6): 104-108.
[14] GUOW, HUANG L J, CHEN C, et al. Elimination of end effects in local mean decomposition using spectral coherence and applications for rotating machinery[J]. Digital Signal Processing, 2016,55:52-63