基于二维多尺度时频散布熵的滚动轴承故障诊断方法

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摘要多尺度散布熵(Multi-scale Dispersion Entropy，MDE1D)是一种有效衡量一维振动信号复杂性特征的非线性动力学分析方法，但其仅能反映振动信号时域中的复杂性特征，无法完整反映振动信号频域的非线性动力学信息。为此，在二维散布熵(Two-dimensional Dispersion Entropy，DE2D)的基础上，论文提出二维时频散布熵(Two-dimensional Time-frequency Dispersion Entropy，TFDE2D)用于衡量时间序列的时频复杂性特征。同时，为了更完整地反映时频分布在不同尺度下的复杂性信息，受多尺度粗粒化启发，将传统粗粒化方法拓展到二维多尺度粗粒化，提出了二维多尺度时频散布熵(Two-dimensional Multi-scale Time-frequency Dispersion Entropy，MTFDE2D)，用来量度振动信号时频分布的多尺度复杂性特征。在此基础上，将其应用于滚动轴承故障诊断中的非线性特征提取，提出了一种基于MTFDE2D和萤火虫优化支持向量机的滚动轴承智能诊断方法。最后，将所提方法应用于滚动轴承实验数据分析，并与现有方法进行对比。结果表明，所提方法不仅能有效地提取故障特征，实现不同轴承故障类型和故障程度的有效诊断，且诊断效果优于对比方法。

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	郑近德
	李嘉绮
	潘海洋
	童靳于
	刘庆运

关键词 ：时频散布熵, 多尺度时频散布熵, 滚动轴承, 萤火虫优化支持向量机, 故障诊断

Abstract：Multi-scale dispersion entropy (MDE1D) is an effective nonlinear dynamics analysis method to measure the complexity characteristics of one-dimensional vibration signal, but it can only reflect the complexity characteristics in the time domain of the vibration signal, and cannot completely reflect the nonlinear dynamics information in the frequency domain of the vibration signal. To this end, two-dimensional time-frequency dispersion entropy (TFDE2D) is proposed based on the two-dimensional dispersion entropy (DE2D) to measure the time-frequency complexity characteristics of time series. Meanwhile, the traditional coarse-grained method is extended to two-dimensional multi-scale coarse-grained to reflect the complexity of the time-frequency distribution at different scales more completely, and the two-dimensional multi-scale time-frequency dispersion entropy (MTFDE2D) is proposed to measure the multi-scale complexity characteristics of the time-frequency distribution of vibration signal. On this basis, an intelligent diagnosis method for rolling bearings based on MTFDE2D and firefly optimized support vector machines is proposed to extraction of nonlinear features in rolling bearing fault diagnosis. Finally, the proposed method is applied to the analysis of experimental data of rolling bearings and compared with the existing methods. The results show that the proposed method can not only be effective in extracting fault characteristics and realize effective diagnosis of different bearing fault types and degrees, but also has better diagnostic effects than the compared methods.

Key words： time-frequency dispersion entropy multi-scale time-frequency dispersion entropy rolling bearing fireflies optimization support vector machine fault diagnosis

收稿日期: 2022-02-24 出版日期: 2023-04-28

引用本文:

郑近德，李嘉绮，潘海洋，童靳于，刘庆运. 基于二维多尺度时频散布熵的滚动轴承故障诊断方法[J]. 振动与冲击, 2023, 42(8): 215-225.
ZHENG Jinde,LI Jiaqi,PAN Haiyang,TONG Jinyu,LIU Qingyun. A two-dimensional multi-scale time-frequency distribution entropy based rolling bearing fault diagnosis method. JOURNAL OF VIBRATION AND SHOCK, 2023, 42(8): 215-225.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2023/V42/I8/215

[1] 徐玉秀,王志强,梅元颖.叶片振动响应的长度分形故障特征提取与诊断[J]. 振动、测试与诊断. 2011, 31(2): 190-192.
XU Yuxiu, WANG Zhiqiang, MEI Yuanying. Fault feature extraction and diagnosis for wind turbine blades from vibration response based on theory of length fractal dimension[J]. Journal of Vibration, Measurement & Diagnosis. 2011, 31(2): 190-192.
[2] 但长林,李三雁,张彬.基于样本熵和SVM的滚动轴承故障诊断方法研究[J]. 中国测试, 2020, 46(11): 37-42.
DAN Changlin, LI Sanyan, ZHANG Bin. Research on fault diagnosis method for rolling element bearings based on sample entropy and SVM[J]. China Measurement & Test, 2020, 46(11): 37-42.
[3] Rostaghi M, Azami H. Dispersion entropy: A measure for time-series analysis[J]. IEEE Signal Processing Letters, 2016, 23(5): 610-614.
[4] Pincus S M. Approximate entropy as a measure of system complexity[J]. Proceedings of the National Academy of Sciences, 1991, 88(6): 2297-2301.
[5] 潘汉明,雷良育,王子辉.基于小波分析的电动汽车轮毂电机永磁体故障诊断[J]. 中国机械工程, 2016, 27(11): 1488-1492.
PAN Hanming, LEI Liangyu, WANG Zihui. Fault diagnosis of permanent magnets of electric car hub motor based on wavelet analysis[J]. China Mechanical Engineering, 2016, 27(11): 1488-1492.
[6] 郑近德,程军圣,胡思宇.多尺度熵在转子故障诊断中的应用[J]. 振动、测试与诊断, 2013, 33(02): 294-297.
ZHENG Jinde, CHENG Junsheng, HU Siyu. Rotor fault diagnosis based on multiscale entropy[J]. Journal of Vibration, Measurement & Diagnosis, 2013, 33(02): 294-297.
[7] 乔新勇,顾程,韩立军.基于VMD多尺度散布熵的柴油机故障诊断方法[J]. 汽车工程, 2020, 42(08): 1139-1144.
QIAO Xinyong, GU Cheng, HAN Lijun. Diesel engine fault diagnosis method based on VMD and multi-scale dispersion entropy[J]. Automotive Engineering, 2020, 42(08): 1139-1144.
[8] 赵建岗,宁静,宁云志,等.基于多尺度排列熵和线性局部切空间排列的机械故障诊断特征提取[J]. 振动与冲击, 2021, 40(13): 136-145.
ZHAO Jiangang, NING Jing, NING Yunzhi, et al. Feature extraction of mechanical fault diagnosis based on MPE-LLTSA[J]. Journal of Vibration and Shock, 2021, 40(13): 136-145.
[9] Silva L E V, Filho A C S, Fazan V P S, et al. Junior, Two-dimensional sample entropy: assessing image texture through irregularity[J]. Biomedical Physics & Engineering Express, 2016, 2(4): 1-11.
[10] Silva L E V, Duque J J, Felipe J C, et al. Two-dimensional multiscale entropy analysis: Applications to image texture evaluation[J]. Signal Processing, 2018, 147: 224-232.
[11] Azami H, Silva L E V, Omoto A C M, et al. Two-dimensional dispersion entropy: An information-theoretic method for irregularity analysis of images[J]. Signal Processing: Image Communication, 2019, 75: 178-187.
[12] 陈小强,沈彦龙,王英,等.基于谱峭度与时频分析的接触线不平顺检测[J]. 振动、测试与诊断, 2021, 41(04): 695-700.
CHENG Xiaoqiang, SHEN Yanlong, WANG Ying, et al. Irregularity detection of contact wire based on spectral kurtosis and time-frequency Analysis.[J]. Journal of Vibration, Measurement & Diagnosis, 2021, 41(04): 695-700.
[13] 刘佳敏,赵知劲,曹越飞,等.基于时频分析的多跳频信号盲检测[J]. 信号处理, 2021, 37(05): 763-771.
LIU Jiamin, ZHAO Zhijin, CAO Yuefei, et al. Blind detection of multi-frequency hopping signals based on time-frequency analysis[J]. Journal of Signal Processing, 2021, 37(05): 763-771.
[14] Richman J S, Moorman J R. Physiological time-series analysis using approximate entropy and sample entropy.[J]. American Journal of Physiology. Heart and Circulatory Physiology, 2000, 278(6): H2039-H2049.
[15] Hilal M, Berthin C, Martin L, et al Bidimensional Multiscale Fuzzy Entropy and Its Application to Pseudoxanthoma Elasticum.[J]. IEEE Transactions on Biomedical Engineering, 2020, 67(7): 2015-2022.
[16] 李从志,郑近德,潘海洋,等.基于自适应多尺度散布熵的滚动轴承故障诊断方法[J]. 噪声与振动控制, 2018, 38(5): 173-179.
LI Congzhi, ZHENG Jinde, PAN Haiyang, et al. Fault diagnosis method of rolling bearings based on adaptive multi-scale dispersion entropy[J]. Nosie and Vibration Control, 2018, 38(5): 173-179.
[17] Wu H C, Zhou J, Xie C H, et al. Two-dimensional time series sample entropy algorithm: Applications to rotor axis orbit feature identification[J]. Mechanical Systems and Signal Processing, 2021, 147:107-123.
[18] 郑近德,陈敏均,程军圣,等.多尺度模糊熵及其在滚动轴承故障诊断中的应用[J]. 振动工程学报, 2014, 27(1): 145-151.
ZHENG Jinde, CHEN Minjun, CHENG Junsheng, et al. Multiscale fuzzy entropy and its application in rolling bearing fault diagnosis[J]. Journal of Vibration Engineering, 2014, 27(1): 145-151.
[19] 蒋玲莉,谭鸿创,李学军,等.基于CEEMDAN排列熵与SVM的螺旋锥齿轮故障识别[J]. 振动.测试与诊断,2021,41(01):33-40.
JIANG Lingli, TAN Hongchuang, LI Xuejun, et al. Fault diagnosis of spiral bevel gear based on CEEMDAN permutation entropy and SVM[J]. Journal of Vibration, Measurement & Diagnosis, 2021, 41(01): 33-40.
[20] YANG X S.Firefly algorithms for mutimodal optimizatiom[M]. Sapporo: Springer, 2009.
[21] Bearing Data Center Website, Case Western Reserve University [DB/OL] [2017-6-20]. http://www.eecs.cwru.edu/laboratory/bearing.
[22] Van der Maaten L, Hinton G. Visualizing data using t-SNE[J]. Journal of machine learning research, 2008, 9(11): 2579-2605.