基于精细复合多尺度散布熵与支持向量机的滚动轴承故障诊断方法
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摘要
为克服多尺度样本熵的不足,更精确地提取滚动轴承非线性故障特征,将一种新的非线性动力学分析方法——精细复合多尺度散布熵引入到滚动轴承的故障特征提取。在此基础上,提出了一种基于精细复合多尺度散布熵与支持向量机的滚动轴承故障诊断新方法。通过滚动轴承实验数据分析,将所提方法与基于多尺度样本熵和多尺度散布熵的故障诊断方法进行了对比,结果表明:所提方法不仅能精确地识别滚动轴承故障类型和故障程度,而且故障识别率高于另两种方法。
In order to overcome the shortcomings of MSE and extract the nonlinear fault features of rolling bearings more accurately, a new nonlinear dynamic analysis method called RCMDE was introduced to the fault feature extraction of rolling bearings. Based on the feature extraction method via RCMDE and support vector machine(SVM), a new method of rolling bearing fault diagnosis was proposed. Through the analysis of rolling bearing experimental data, the proposed method was compared with the multiscale dispersion entropy and MSE based fault diagnosis methods and the results show that the fault diagnosis method may accurately identify the fault locations and severities of rolling bearings, and has a higher fault recognition rate than that of the two methods mentioned.
In order to overcome the shortcomings of MSE and extract the nonlinear fault features of rolling bearings more accurately, a new nonlinear dynamic analysis method called RCMDE was introduced to the fault feature extraction of rolling bearings. Based on the feature extraction method via RCMDE and support vector machine(SVM), a new method of rolling bearing fault diagnosis was proposed. Through the analysis of rolling bearing experimental data, the proposed method was compared with the multiscale dispersion entropy and MSE based fault diagnosis methods and the results show that the fault diagnosis method may accurately identify the fault locations and severities of rolling bearings, and has a higher fault recognition rate than that of the two methods mentioned.
引文
[1]刘中磊,于德介,刘坚.基于故障特征频率的阶比双谱方法及其在滚动轴承故障诊断中的应用[J].中国机电工程学报,2013,33(33):123-129.LIU Zhonglei,YU Dejie,LIU Jian.Order Bispectrum Analysis Based on Fault Characteristic Frequency and Its Application to the Fault Diagnosis of Rolling Bearings[J].Proceedings of the CSEE,2013,33(33):123-129.
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[8]刘慧,谢洪波,和卫星,等.基于模糊熵的脑电睡眠分期特征提取与分类[J].数据采集与处理,2010,25(4):484-489.LIU Hui,XIE Hongbo,HE Weixing,et al.Characterization and Classification of EEG Sleep Stage Based on Fuzzy Entropy[J].Journal of Data Acquisition&Processing,2010,25(4):484-489.
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[10]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.
[11]郑近德,程军圣,杨宇.多尺度排列熵及其在滚动轴承故障诊断中的应用[J].中国机械工程,2013,24(19):2641-2646.ZHENG Jinde,CHENG Junsheng,YANG Yu.Multi-scale Permutation Entropy and Its Applications to Rolling Bearing Fault Diagnosis[J].China Mechanical Engineering,2013,24(19):2641-2646.
[12]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(2):294-297.ZHENG Jinde,CHENG Junsheng,HU Siyu.Multiscale Entropy Based Rotor Fault Diagnosis[J].Journal of Vibration Measurement&Diagnosis,2013,33(2):294-297.
[13]ROSTAGHI M,AZAMI H.Dispersion Entropy:a Measure for Time-series Analysis[J].IEEE Signal Processing Letters,2016,23(5):610-614.
[14]AZAMI H,ROSTAGHI M,ABASOLO D,et al.Refined Composite Multiscale Dispersion Entropy and Its Application to Biomedical Signals[J].IEEETransactions on Biomedical Engineering,2017,64(12):2872-2879.
[15]CHANG C C,LIN C J.LIBSVM:a Library for Support Vector Machines[EB/OL](2016-12-22)[2017-6-20].http:∥www.csie.ntu.edu.tw/~cjlin/libsvm.
[16]Bearing Data Center Website.Case Western Reserve University[DB/OL][2017-6-20].http:∥www.eecs.cwru.edu/laboratory/bearing.
[2]梁瑜,贾利民,蔡国强,等.滚动轴承的非线性动力学故障模型研究[J].中国铁道科学,2014,35(1):98-104.LIANG Yu,JIA Limin,CAI Guoqiang,et al.Research on Nonlinear Dynamics Fault Model of Rolling Bearing[J].China Railway Science,2014,35(1):98-104.
[3]吕志民,徐金梧,翟绪圣.分形维数及其在滚动轴承故障诊断中的应用[J].机械工程学报,1999,2(2):1-8.LYU Zhimin,XU Jinwu,ZHAI Xusheng.Fractal Dimension and Its Application in Fault Diagnosis of Rolling Bearing[J].Chinese Journal of Mechanical Engineering,1999,2(2):1-8.
[4]赵志宏,杨绍普.基于小波包变换与样本熵的滚动轴承故障诊断[J].振动、测试与诊断,2012,32(4):640-644.ZHAO Zhihong,YANG Shaopu.Roller Bearing Fault Diagnosis Based on Wavelet Packet Transform and Sample Entropy[J].Journal of Vibration Measurement&Diagnosis,2012,32(4):640-644.
[5]YAN Ruqiang,LIU Yongbin,GAO R X.Permutation Entropy:a Nonlinear Statistical Measure for Status Characterization of Rotary Machines[J].Mechanical Systems&Signal Processing,2012,29(5):474-484.
[6]何志坚,周志雄.基于ELMD的样本熵及BoostingSVM的滚动轴承故障诊断[J].振动与冲击,2016,35(18):190-195.HE Zhijian,ZHOU Zhixiong.Fault Diagnosis of Roller Bearings Based on ELMD Sample Entropy and Boosting-SVM[J].Journal of Vibration and Shock,2016,35(18):190-195.
[7]PINCUS S M.Approximate Entropy as a Measure of System Complexity[J].Proceedings of the National Academy of Sciences,1991,88(6):2297-2301.
[8]刘慧,谢洪波,和卫星,等.基于模糊熵的脑电睡眠分期特征提取与分类[J].数据采集与处理,2010,25(4):484-489.LIU Hui,XIE Hongbo,HE Weixing,et al.Characterization and Classification of EEG Sleep Stage Based on Fuzzy Entropy[J].Journal of Data Acquisition&Processing,2010,25(4):484-489.
[9]姜万录,李扬,郑直,等.基于迭代自适应多尺度形态滤波的滚动轴承故障诊断[J].中国机械工程,2015,26(23):3192-3199.JIANG Wanlu,LI Yang,ZHENG Zhi,et al.Iterative Adaptive Multiscale Morphological Filtering-based Fault Diagnosis for Rolling Bearings[J].China Mechanical Engineering,2015,26(23):3192-3199.
[10]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.
[11]郑近德,程军圣,杨宇.多尺度排列熵及其在滚动轴承故障诊断中的应用[J].中国机械工程,2013,24(19):2641-2646.ZHENG Jinde,CHENG Junsheng,YANG Yu.Multi-scale Permutation Entropy and Its Applications to Rolling Bearing Fault Diagnosis[J].China Mechanical Engineering,2013,24(19):2641-2646.
[12]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(2):294-297.ZHENG Jinde,CHENG Junsheng,HU Siyu.Multiscale Entropy Based Rotor Fault Diagnosis[J].Journal of Vibration Measurement&Diagnosis,2013,33(2):294-297.
[13]ROSTAGHI M,AZAMI H.Dispersion Entropy:a Measure for Time-series Analysis[J].IEEE Signal Processing Letters,2016,23(5):610-614.
[14]AZAMI H,ROSTAGHI M,ABASOLO D,et al.Refined Composite Multiscale Dispersion Entropy and Its Application to Biomedical Signals[J].IEEETransactions on Biomedical Engineering,2017,64(12):2872-2879.
[15]CHANG C C,LIN C J.LIBSVM:a Library for Support Vector Machines[EB/OL](2016-12-22)[2017-6-20].http:∥www.csie.ntu.edu.tw/~cjlin/libsvm.
[16]Bearing Data Center Website.Case Western Reserve University[DB/OL][2017-6-20].http:∥www.eecs.cwru.edu/laboratory/bearing.
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