基于Alaph稳定分布与多重分形分析的齿轮箱故障特征提取方法研究进展
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摘要
齿轮箱工作环境恶劣,齿轮与滚动轴承等关键部件易发生疲劳故障。将目前常用的故障特征提取方法应用于齿轮箱实际诊断时,其结果具有不稳定性。Alpha稳定分布与多重分形分析被逐渐应用于故障诊断领域,这2种方法各具优点且相互关联。文章对Alpha稳定分布及多重分形分析应用于齿轮箱齿轮或滚动轴承故障特征提取的已有成果进行详细梳理,分别从基于Alpha稳定分布的故障特征提取方法、基于多重分形的故障特征提取方法及基于Alpha稳定分布与多重分形的特征融合方法 3方面进行评述,并指出今后可进一步在特征筛选、特征融合等方面开展研究。
Gearbox is usually designed to operate on complex conditions with variable speeds,loading and temperatures,which easily lead to fatigue faults of its key components,such as gears and rolling bearings. At present,the diagnosis result is occasionally unstable when the common fault feature extraction methods are used in actual diagnosis of gearbox. Recently,Alpha stable distribution( ASD) and Multi-fractal analysis( MFA) have been investigated to address this limitation. These two methods have their respective advantages,and can be compensated each other. This article reviews and analyzes the existing achievements,and discusses the three aspects of the fault feature extraction method based on ASD,the fault feature extraction method based on MFA,and the fault feature extraction method based on feature fusion of ASD and MFA,respectively. Finally,the future research directions in feature selection,feature fusion are pointed out.
Gearbox is usually designed to operate on complex conditions with variable speeds,loading and temperatures,which easily lead to fatigue faults of its key components,such as gears and rolling bearings. At present,the diagnosis result is occasionally unstable when the common fault feature extraction methods are used in actual diagnosis of gearbox. Recently,Alpha stable distribution( ASD) and Multi-fractal analysis( MFA) have been investigated to address this limitation. These two methods have their respective advantages,and can be compensated each other. This article reviews and analyzes the existing achievements,and discusses the three aspects of the fault feature extraction method based on ASD,the fault feature extraction method based on MFA,and the fault feature extraction method based on feature fusion of ASD and MFA,respectively. Finally,the future research directions in feature selection,feature fusion are pointed out.
引文
[1]张梅军.机械状态检测与故障诊断[M].北京:国防工业出版社,2008.
[2]丁康,李巍华,朱小勇.齿轮及齿轮箱故障诊断实用技术[M].北京:机械工业出版社,2005:1.
[3]黄文虎,夏松波,刘瑞岩.设备故障诊断原理技术及应用[M].北京:科学出版社,1996:2.
[4]胡爱军.变换在旋转机械振动信号分析中的应用研究[D].北京:华北电力大学,2008.
[5]YAN X A,JIA M P,XIANG L.Compound fault diagnosis of rotating machinery based on OVMD and a 1.5-dimension envelope spectrum[J].Measurement Science&Technology,2016,27(7):1.
[6]CHEN X L,FENG F Z,ZHANG B Z.Weak fault feature extraction of rolling bearings based on an improved kurtogram[J].Sensors,2016,16(9):1.
[7]WANG X,ZHENG Y,ZHAO Z Z,et al.Bearing fault diagnosis based on statistical locally linear embedding[J].Sensors,2015,15(7):16225.
[8]CAI J H,CHEN Q Y.Bearing fault diagnosis method based on local mean decomposition and wigner higher moment spectrum[J].Experimental Techniques,2016,40(5):1.
[9]WANG Y,XU G H,LIANG L,et al.Detection of weak transient signals based on wavelet packet transform and manifold learning for rolling element bearing fault diagnosis[J].Mechanical Systems and Signal Processing,2015,54/55:259.
[10]HAN Te,JIANG Dongxiang,WANG Nanfei,et al.The fault feature extraction of rolling bearing based on emd and difference spectrum of singular value[J].Shock&Vibration,2016(13):1.
[11]CHENG Gang,CHEN Xihui,LI Hongyu,et al.Study on planetary gear fault diagnosis based on entropy feature fusion of ensemble empirical mode decomposition[J].Measurement,2016,91:140.
[12]WU Z H,HUANG N E.Ensemble empirical mode decomposition:a noise-assisted data analysis method[J].Advances in Adaptive Data Analysis,2009,1(1):1.
[13]杨伟超,赵春晖,成宝芝.Alpha稳定分布噪声下的通信信号识别[J].应用科学学报,2010,28(2):111.
[14]张军保.基于对称alpha平稳分布的SAR图像复原算法研究[D].北京:中国科学院研究生院,2005.
[15]胡睿,孙进平,王文光.基于α稳定分布的SAR图像目标检测算法[J].中国图象图形学报,2009,14(1):25.
[16]余香梅,舒彤.基于α稳定分布参数和支持向量机的齿轮箱故障诊断方法[J].测控技术,2012,31(8):23.
[17]YU Gang,SHI Ningning.Gear fault signal modeling and detection based on alpha stable distribution[C]//2012 International Symposium on Instrumentation&Measurement,Sensor Network and Automatic(IMSNA).Piscataway,NJ,USA:IEEE,2012:471-474.
[18]李长宁.机械故障信号统计建模及其故障诊断方法的研究[D].哈尔滨:哈尔滨工业大学,2010.
[19]YU Gang,LI Changning,ZHANG Jianfeng.A new statistical modeling and detection method for rolling element bearing faults based on alpha-stable distribution[J].Mechanical Systems and Signal Processing,2013,41(1):155.
[20]SAMORODNITSKY Gennady,TAQQU M S.Stable nonGaussian random processes:stochastic models with infinite variance[M].New York,London:Chapman and Hall,1994.
[21]LI Yongjian,ZHANG Weihua,XIONG Qing,et al.A novel fault diagnosis model for bearing of railway vehicles using vibration signals based on symmetric alpha-stable distribution feature extraction[J].Shock and Vibration,2016(20):1.
[22]XIONG Qing,XU Yanhai,PENG Yiqiang,et al.Low-speed rolling bearing fault diagnosis based on EMD denoising and parameter estimate with alpha stable distribution[J].Journal of Mechanical Science&Technology,2017,31(4):1587.
[23]师宁宁.基于Alpha稳定分布参数的齿轮故障建模及检测[D].哈尔滨:哈尔滨工业大学,2012.
[24]ZHANG Xinghui,KANG Jianshe,XIAO Lei,et al.Alpha stable distribution based morphological filter for bearing and gear fault diagnosis in nuclear power plant[J].Science and Technology of Nuclear Installations,2015(1):1.
[25]申永军,段春宇,王杜娟,等.基于α稳定分布和支持向量机的轴承模式分类[J].振动、测试与诊断,2015,35(6):1068.
[26]CHOURI B,AROUSSI M E,TABAA M,et al.Bearing fault diagnosis based on Alpha-stable distribution feature extraction and SVM classifier[C]//International Conference on Multimedia Computing&Systems.[S.l.]:IEEE,2015:277-280.
[27]熊庆,张卫华,陆天炜,等.基于α稳定分布参数估计的滚动轴承故障诊断[J].振动、测试与诊断,2015,35(2):238.
[28]辛厚文.分形理论及其应用[M].合肥:中国科技大学出版社,1999.
[29]KANTELHARDT J W,ZSCHIEGNER S A,KOSCIELNYBUNDE E,et al.Multi-fractal detrended fluctuation analysis of nonstationary time series[J].Physica A,2002,316(1):87.
[30]李兆飞,柴毅,李华锋.多重分形去趋势波动分析的振动信号故障诊断[J].华中科技大学学报(自然科学版),2012,40(12):5.
[31]李鹏.多尺度分析方法在旋转机械状态监测中的应用研究[D].合肥:中国科学技术大学,2012.
[32]林近山,陈前.基于多重分形去趋势波动分析的齿轮箱故障特征提取方法[J].振动与冲击,2013,32(2):97.
[33]朱云博,冯广斌,孙华刚,等.基于多重分形与SVM的齿轮箱故障诊断研究[J].机械传动,2012,36(6):99.
[34]LIN Jinshan,CHEN Qian.Fault diagnosis of rolling bearings based on multifractal detrended?uctuation analysis and Mahalanobis distance criterion[J].Mechanical Systems and Signal Processing,2013,38:515.
[35]LIU Hongmei,WANG Xuan,LU Chen.Rolling bearing fault diagnosis based on LCD-TEO and multifractal detrended fluctuation analysis[J].Mechanical Systems and Signal Processing,2015,60/61:273.
[36]赵玉春.基于混沌分形与模糊聚类的滚动轴承故障诊断[D].秦皇岛:燕山大学,2011.
[37]LI Meng,MA Wenxing,LIU Xiujuan.Investigation of rolling bearing fault diagnosis based on multi-fractal and general fractal dimension[J].International Conference on Intelligent Computation Technology and Automation,2009,2:545.
[38]HU Jinqiu,ZHANG Laibin,LIANG Wei,et al.Incipient mechanical fault detection based on multifractal and MTS methods[J].Petroleum Science,2009,6(2):208.
[39]贾峰,武兵,熊晓燕,等.基于EMD与多重分形去趋势法的轴承智能诊断方法[J].中南大学学报(自然科学版),2015,46(2):491.
[40]李莎,潘宏侠,张君东,等.基于多重分形和PSO-SVM的齿轮箱故障诊断[J].机械传动,2015,39(2):132.
[41]褚青青,肖涵,吕勇,等.基于多重分形理论与神经网络的齿轮故障诊断[J].振动与冲击,2015,34(21):15.
[42]熊庆,张卫华.基于MF-DFA与PSO优化LSSVM的滚动轴承故障诊断方法[J].振动与冲击,2015,34(11):188.
[43]李学军,李平,蒋玲莉,等.基于异类信息特征融合的异步电机故障诊断[J].仪器仪表学报,2013,34(1):227.
[44]胡建中,吴瑶,谢小欣,等.一种基于LLE特征融合的故障识别方法[J].中国机械工程,2013,24(24):3345.
[45]张恒,赵荣珍.故障特征选择与特征信息融合的加权KPCA方法研究[J].振动与冲击,2014,33(9):89.
[46]王晶,陈果,郝腾飞.滚动轴承早期故障的多源多方法融合诊断技术[J].振动、测试与诊断,2013,33(5):868.
[47]ESPEN A F I.The influence of power law distributions on long-range trial dependency of response times[J].Journal of Mathematical Psychology,2013,57(5):215.
[48]杨伟超.Alpha稳定分布噪声下通信信号调制识别研究[D].哈尔滨:哈尔滨工程大学,2012.
[49]XIONG Qing,ZHANG Weihua,LU Tianwei,et al.A fault diagnosis method for rolling bearings based on feature fusion of Multifractal detrended fluctuation analysis and Alpha stable distribution[J].Shock and Vibration,2016(3):1.
[2]丁康,李巍华,朱小勇.齿轮及齿轮箱故障诊断实用技术[M].北京:机械工业出版社,2005:1.
[3]黄文虎,夏松波,刘瑞岩.设备故障诊断原理技术及应用[M].北京:科学出版社,1996:2.
[4]胡爱军.变换在旋转机械振动信号分析中的应用研究[D].北京:华北电力大学,2008.
[5]YAN X A,JIA M P,XIANG L.Compound fault diagnosis of rotating machinery based on OVMD and a 1.5-dimension envelope spectrum[J].Measurement Science&Technology,2016,27(7):1.
[6]CHEN X L,FENG F Z,ZHANG B Z.Weak fault feature extraction of rolling bearings based on an improved kurtogram[J].Sensors,2016,16(9):1.
[7]WANG X,ZHENG Y,ZHAO Z Z,et al.Bearing fault diagnosis based on statistical locally linear embedding[J].Sensors,2015,15(7):16225.
[8]CAI J H,CHEN Q Y.Bearing fault diagnosis method based on local mean decomposition and wigner higher moment spectrum[J].Experimental Techniques,2016,40(5):1.
[9]WANG Y,XU G H,LIANG L,et al.Detection of weak transient signals based on wavelet packet transform and manifold learning for rolling element bearing fault diagnosis[J].Mechanical Systems and Signal Processing,2015,54/55:259.
[10]HAN Te,JIANG Dongxiang,WANG Nanfei,et al.The fault feature extraction of rolling bearing based on emd and difference spectrum of singular value[J].Shock&Vibration,2016(13):1.
[11]CHENG Gang,CHEN Xihui,LI Hongyu,et al.Study on planetary gear fault diagnosis based on entropy feature fusion of ensemble empirical mode decomposition[J].Measurement,2016,91:140.
[12]WU Z H,HUANG N E.Ensemble empirical mode decomposition:a noise-assisted data analysis method[J].Advances in Adaptive Data Analysis,2009,1(1):1.
[13]杨伟超,赵春晖,成宝芝.Alpha稳定分布噪声下的通信信号识别[J].应用科学学报,2010,28(2):111.
[14]张军保.基于对称alpha平稳分布的SAR图像复原算法研究[D].北京:中国科学院研究生院,2005.
[15]胡睿,孙进平,王文光.基于α稳定分布的SAR图像目标检测算法[J].中国图象图形学报,2009,14(1):25.
[16]余香梅,舒彤.基于α稳定分布参数和支持向量机的齿轮箱故障诊断方法[J].测控技术,2012,31(8):23.
[17]YU Gang,SHI Ningning.Gear fault signal modeling and detection based on alpha stable distribution[C]//2012 International Symposium on Instrumentation&Measurement,Sensor Network and Automatic(IMSNA).Piscataway,NJ,USA:IEEE,2012:471-474.
[18]李长宁.机械故障信号统计建模及其故障诊断方法的研究[D].哈尔滨:哈尔滨工业大学,2010.
[19]YU Gang,LI Changning,ZHANG Jianfeng.A new statistical modeling and detection method for rolling element bearing faults based on alpha-stable distribution[J].Mechanical Systems and Signal Processing,2013,41(1):155.
[20]SAMORODNITSKY Gennady,TAQQU M S.Stable nonGaussian random processes:stochastic models with infinite variance[M].New York,London:Chapman and Hall,1994.
[21]LI Yongjian,ZHANG Weihua,XIONG Qing,et al.A novel fault diagnosis model for bearing of railway vehicles using vibration signals based on symmetric alpha-stable distribution feature extraction[J].Shock and Vibration,2016(20):1.
[22]XIONG Qing,XU Yanhai,PENG Yiqiang,et al.Low-speed rolling bearing fault diagnosis based on EMD denoising and parameter estimate with alpha stable distribution[J].Journal of Mechanical Science&Technology,2017,31(4):1587.
[23]师宁宁.基于Alpha稳定分布参数的齿轮故障建模及检测[D].哈尔滨:哈尔滨工业大学,2012.
[24]ZHANG Xinghui,KANG Jianshe,XIAO Lei,et al.Alpha stable distribution based morphological filter for bearing and gear fault diagnosis in nuclear power plant[J].Science and Technology of Nuclear Installations,2015(1):1.
[25]申永军,段春宇,王杜娟,等.基于α稳定分布和支持向量机的轴承模式分类[J].振动、测试与诊断,2015,35(6):1068.
[26]CHOURI B,AROUSSI M E,TABAA M,et al.Bearing fault diagnosis based on Alpha-stable distribution feature extraction and SVM classifier[C]//International Conference on Multimedia Computing&Systems.[S.l.]:IEEE,2015:277-280.
[27]熊庆,张卫华,陆天炜,等.基于α稳定分布参数估计的滚动轴承故障诊断[J].振动、测试与诊断,2015,35(2):238.
[28]辛厚文.分形理论及其应用[M].合肥:中国科技大学出版社,1999.
[29]KANTELHARDT J W,ZSCHIEGNER S A,KOSCIELNYBUNDE E,et al.Multi-fractal detrended fluctuation analysis of nonstationary time series[J].Physica A,2002,316(1):87.
[30]李兆飞,柴毅,李华锋.多重分形去趋势波动分析的振动信号故障诊断[J].华中科技大学学报(自然科学版),2012,40(12):5.
[31]李鹏.多尺度分析方法在旋转机械状态监测中的应用研究[D].合肥:中国科学技术大学,2012.
[32]林近山,陈前.基于多重分形去趋势波动分析的齿轮箱故障特征提取方法[J].振动与冲击,2013,32(2):97.
[33]朱云博,冯广斌,孙华刚,等.基于多重分形与SVM的齿轮箱故障诊断研究[J].机械传动,2012,36(6):99.
[34]LIN Jinshan,CHEN Qian.Fault diagnosis of rolling bearings based on multifractal detrended?uctuation analysis and Mahalanobis distance criterion[J].Mechanical Systems and Signal Processing,2013,38:515.
[35]LIU Hongmei,WANG Xuan,LU Chen.Rolling bearing fault diagnosis based on LCD-TEO and multifractal detrended fluctuation analysis[J].Mechanical Systems and Signal Processing,2015,60/61:273.
[36]赵玉春.基于混沌分形与模糊聚类的滚动轴承故障诊断[D].秦皇岛:燕山大学,2011.
[37]LI Meng,MA Wenxing,LIU Xiujuan.Investigation of rolling bearing fault diagnosis based on multi-fractal and general fractal dimension[J].International Conference on Intelligent Computation Technology and Automation,2009,2:545.
[38]HU Jinqiu,ZHANG Laibin,LIANG Wei,et al.Incipient mechanical fault detection based on multifractal and MTS methods[J].Petroleum Science,2009,6(2):208.
[39]贾峰,武兵,熊晓燕,等.基于EMD与多重分形去趋势法的轴承智能诊断方法[J].中南大学学报(自然科学版),2015,46(2):491.
[40]李莎,潘宏侠,张君东,等.基于多重分形和PSO-SVM的齿轮箱故障诊断[J].机械传动,2015,39(2):132.
[41]褚青青,肖涵,吕勇,等.基于多重分形理论与神经网络的齿轮故障诊断[J].振动与冲击,2015,34(21):15.
[42]熊庆,张卫华.基于MF-DFA与PSO优化LSSVM的滚动轴承故障诊断方法[J].振动与冲击,2015,34(11):188.
[43]李学军,李平,蒋玲莉,等.基于异类信息特征融合的异步电机故障诊断[J].仪器仪表学报,2013,34(1):227.
[44]胡建中,吴瑶,谢小欣,等.一种基于LLE特征融合的故障识别方法[J].中国机械工程,2013,24(24):3345.
[45]张恒,赵荣珍.故障特征选择与特征信息融合的加权KPCA方法研究[J].振动与冲击,2014,33(9):89.
[46]王晶,陈果,郝腾飞.滚动轴承早期故障的多源多方法融合诊断技术[J].振动、测试与诊断,2013,33(5):868.
[47]ESPEN A F I.The influence of power law distributions on long-range trial dependency of response times[J].Journal of Mathematical Psychology,2013,57(5):215.
[48]杨伟超.Alpha稳定分布噪声下通信信号调制识别研究[D].哈尔滨:哈尔滨工程大学,2012.
[49]XIONG Qing,ZHANG Weihua,LU Tianwei,et al.A fault diagnosis method for rolling bearings based on feature fusion of Multifractal detrended fluctuation analysis and Alpha stable distribution[J].Shock and Vibration,2016(3):1.