列车转向架轴承服役过程监测与故障诊断系统研究
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摘要
轴箱轴承作为列车转向架的关键部件之一,其服役状况对行车安全有着重大影响。实时有效的轴箱轴承在线监测系统不仅可以避免列车事故的发生,还能实现以状态修来替代目前普遍采用的时间修的列车轴箱轴承维修机制,从而降低列车的运营成本。本文以列车转向架轴箱轴承服役过程监测与故障诊断系统为研究对象,依据系统各个子系统的功能特性,开展了轴箱轴承振动信号采集、信号处理分析、故障特征提取以及故障模式识别等方面的深入研究。
信号采集实现了列车轴箱轴承振动信号及速度信号的拾取。’论文研究了振动信号的特性,实现了振动信号放大以及基于二阶切比雪夫和二阶巴特沃兹滤波器级联的抗混叠滤波器设计,并根据故障诊断系统信号分析的需求,设计了基于AD7608的模数转换电路。
信号处理分析实现了轴箱轴承振动信号消噪和故障模态分析。本文采用了基于小波变换的方法实现振动信号中的消噪处理,有效地去除了噪声对故障信号的干扰。Hilbert-Huang变换是基于信号自身特性的自适应的时频分析方法,本文针对轴箱轴承振动信号非线性、非平稳性的特性,提出了基于Hilbert-Huang变换的列车轴箱轴承故障诊断方法。该方法采用Hilbert-Huang变换对信号进行EMD分解和谱分析,得到了具有明显故障特征的Hilbert谱和Hilbert边际谱,结果表明Hilbert-Huang变换可以有效地对故障进行识别。
故障特征提取是提取能有效反映轴箱轴承故障的特征向量。本文对列车轴箱轴承的故障振动信号特征进行了深入研究,提取了能够表征不同故障类型的多特征参量。这些特征参量包括时域统计参数、固有模态函数能量矩以及故障特征幅值比,能够全面地反映轴箱轴承的健康状况。
最后论文设计了基于BP神经网络的故障分类器用于故障模式识别。BP神经网络实现了轴箱轴承多特征参量到故障状态之间的非线性映射,对轴承正常、外圈故障、内圈故障及滚动体故障四类工况进行有效识别,实验结果表明,诊断正确率达到了90%以上。
Axlebox bearing is one of the key components of train bogie. The condition of the axlebox bearing has great influence on rail traffic security. A real time and effective axlebox bearing fault diagnosis method could avoid traffic accidents and reduce operation costs. By this way, we can adopt condition-based maintenance instead of preventive maintenance which is now being widely adopted. This thesis takes service process monitoring and fault diagnosis system as the research object, studies the signal acquisition, signal processing analysis, fault feature extraction and pattern recognition.
Signal acquisition implements the function of packing up vibration and speed signal. Anti-aliasing filter based on second order chebyshev filter and butterworth filter and AD conversion circuit based on AD7608are designed.
Signal processing analysis implements the function of vibration signal denoising and fault modal analysis. The thesis adopts wavelet denoising method to remove noise in vibration signal. Hilbert-Huang Transform is an adaptive time-frequency analysis method based on the signal itself. Aiming at the characteristic of vibration signal, an axlebox bearing fault diagnosis method based on Hilbert-Huang Transform is proposed. The Hilbert spectrum and Hilbert marginal spectrum with obvious fault features could be obtained by Hilbert-Huang Transform. The results show that the fault diagnosis method based on Hilbert-Huang Transform can effectively identify the fault.
Fault feature extraction implements the function of eigenvectors extraction which could reflect the fault of axlebox bearing. This thesis extracts multi-feature parameters including time domain parameters, energy moments of IMF and fault characteristic amplitude ratio, which could reflect the health status of the axlebox bearing entirely.
Fault classifier based on BP Neural Network is designed for fault pattern recognition to realize the nonlinear mapping between multi-feature parameters and fault state. To classify different conditions of axlebox bearing, the diagnostic accuracy is above90%.
信号采集实现了列车轴箱轴承振动信号及速度信号的拾取。’论文研究了振动信号的特性,实现了振动信号放大以及基于二阶切比雪夫和二阶巴特沃兹滤波器级联的抗混叠滤波器设计,并根据故障诊断系统信号分析的需求,设计了基于AD7608的模数转换电路。
信号处理分析实现了轴箱轴承振动信号消噪和故障模态分析。本文采用了基于小波变换的方法实现振动信号中的消噪处理,有效地去除了噪声对故障信号的干扰。Hilbert-Huang变换是基于信号自身特性的自适应的时频分析方法,本文针对轴箱轴承振动信号非线性、非平稳性的特性,提出了基于Hilbert-Huang变换的列车轴箱轴承故障诊断方法。该方法采用Hilbert-Huang变换对信号进行EMD分解和谱分析,得到了具有明显故障特征的Hilbert谱和Hilbert边际谱,结果表明Hilbert-Huang变换可以有效地对故障进行识别。
故障特征提取是提取能有效反映轴箱轴承故障的特征向量。本文对列车轴箱轴承的故障振动信号特征进行了深入研究,提取了能够表征不同故障类型的多特征参量。这些特征参量包括时域统计参数、固有模态函数能量矩以及故障特征幅值比,能够全面地反映轴箱轴承的健康状况。
最后论文设计了基于BP神经网络的故障分类器用于故障模式识别。BP神经网络实现了轴箱轴承多特征参量到故障状态之间的非线性映射,对轴承正常、外圈故障、内圈故障及滚动体故障四类工况进行有效识别,实验结果表明,诊断正确率达到了90%以上。
Axlebox bearing is one of the key components of train bogie. The condition of the axlebox bearing has great influence on rail traffic security. A real time and effective axlebox bearing fault diagnosis method could avoid traffic accidents and reduce operation costs. By this way, we can adopt condition-based maintenance instead of preventive maintenance which is now being widely adopted. This thesis takes service process monitoring and fault diagnosis system as the research object, studies the signal acquisition, signal processing analysis, fault feature extraction and pattern recognition.
Signal acquisition implements the function of packing up vibration and speed signal. Anti-aliasing filter based on second order chebyshev filter and butterworth filter and AD conversion circuit based on AD7608are designed.
Signal processing analysis implements the function of vibration signal denoising and fault modal analysis. The thesis adopts wavelet denoising method to remove noise in vibration signal. Hilbert-Huang Transform is an adaptive time-frequency analysis method based on the signal itself. Aiming at the characteristic of vibration signal, an axlebox bearing fault diagnosis method based on Hilbert-Huang Transform is proposed. The Hilbert spectrum and Hilbert marginal spectrum with obvious fault features could be obtained by Hilbert-Huang Transform. The results show that the fault diagnosis method based on Hilbert-Huang Transform can effectively identify the fault.
Fault feature extraction implements the function of eigenvectors extraction which could reflect the fault of axlebox bearing. This thesis extracts multi-feature parameters including time domain parameters, energy moments of IMF and fault characteristic amplitude ratio, which could reflect the health status of the axlebox bearing entirely.
Fault classifier based on BP Neural Network is designed for fault pattern recognition to realize the nonlinear mapping between multi-feature parameters and fault state. To classify different conditions of axlebox bearing, the diagnostic accuracy is above90%.
引文
[1]黄采伦,樊晓平,陈特放.列车故障在线诊断技术及应用[M].北京：国防工业出版社,2006：4-5
[2]盛兆顺、尹琦岭.设备状态监测与故障诊断技术及应用[M].北京：化学工业出版社,2003：224-225
[3]曾刚.滚动轴承微弱故障信号检测方法研究[硕士学位论文].成都.成都理工大学.2010
[4]梅宏斌.滚动轴承振动监测与诊断[M].北京：机械工业出版社,1995：1-10
[5]苏文胜.滚动轴承振动信号处理及特征提取方法研究[博士学位论文].大连.大连理工大学.2010
[6]葛哲学,陈仲生MATLAB时频分析技术及其应用[M].北京：人民邮电出版社,2006：2-5
[7]屈梁生,张西宁,沈玉娣.机械故障诊断理论与方法[M].西安：西安交通大学出版社,2009：117-118
[8]李舜酩,李香莲.振动信号的现代分析技术与应用[M].北京：国防工业出版社,2008：57-59
[9]Norden E. Huang, Zheng Shen, Steven R. Long, et al.The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis. Proc.Roy.Soc. Lond.1998.454 (A):903-993
[10]Jon S. Wilson.林龙信,邓彬,张鼎等译.传感器技术手册[M].北京：人民邮电出版社,2009：222-224
[11]SKF, Vibration Diagnostic Guide, http://www.skf.com/portal/skf/home/aptitudexchange? contentId=0.237932.237933.237934.237960.867870, 2009
[12]张德丰.Matlab小波分析[M].北京：机械工业出版社,2009：50-51
[13]彭志科.小波分析在旋转设备故障诊断中的应用[博士学位论文].北京.清华大学.2002
[14]潘泉,张磊,孟晋丽等.小波滤波方法及应用[M].北京：清华大学出版社,2005：6-7
[15]蒋永华.旋转机械非平稳信号微弱特征提取方法研究[博士学位论文].重庆.重庆大学.2010
[16]胡昌华,张军波,夏军等.基于MATLAB的系统分析与设计-小波分析[M].西安：西安电子科技大学出版社,1999：5-6
[17]Norden E. Huang, Samuel S P Shen. Hilbert-Huang Transform and Its Applications. Hackensack. World Scientific Publishing [M].2005:1-2
[18]Norden E. Huang, Nii O. Attoh-Okine. The Hilbert-Huang Transform in Engineering[M]. Boca Raton. CRC Press.2005:3-4
[19]Gabriel Rilling, Patrick Flandrin, Paulo Goncalves. On Empirical Mode Decomposition and Its Algorithms. Proc. IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing, 2003:8-11
[20]黄大吉,赵进平,苏纪兰.希尔伯特-黄变换的端点延拓[J].海洋学报.2003,25(1)：1-11
[21]Yu Yang, Dejie Yu, Junsheng Cheng. A fault diagnosis approach for roller bearing based on IMF envelope spectrum and SVM[J]. Measurement, 2007,40(9-10):943-950
[22]Z.PENG, F.CHU, Y.HE. Vibration signal analysis and feature extraction based on reassigned wavelet scalogram [J]. Journal of Sound and Vibration, 2002, 253(5):1087-1100
[23]周福昌,陈进,何俊等.循环平稳信号处理在机械设备故障诊断中的应用综述[J].振动 与冲击.2006,25(5)：148-152
[24]G.F. Bin, J.J. Gao, X.J. Li, et al. Early fault diagnosis of rotating machinery based on wavelet packets-Empirical mode decomposition feature extraction and neural network [J]. Mechanical Systems and Signal Processing, 2012,27:696-711
[25]Yuping Zhang, Shufeng Ai. EMD based envelope analysis for bearing faults detection [A]. In: 7th World Congress on Intelligent Control and Automation [C].2008:4257-4260
[26]韩捷,张瑞林.旋转机械故障机理及诊断技术[M].北京：机械工业出版社,1997：37-38
[27]胡婧.基于BP神经网络的滚动轴承缺陷诊断研究[硕士学位论文].武汉.华中科技大学.2006
[28]于德介,程军圣,杨宇.机械故障诊断的Hilbert-Huang变换方法[M].北京：科学出版社,2006：154-155
[29]程军圣.基于Hilbert-Huang变换的旋转机械故障诊断方法研究[博士学位论文].长沙.湖南大学.2005
[30]杨勇.EMD和模糊神经网络在滚动轴承故障诊断中的研究与应用[硕士学位论文].太原.太原理工大学.2008
[31]Yang Yu, Yu Dejie, Cheng Junsheng. A roller bearing fault diagnosis method based on EMD energy entropy and ANN [J]. Journal of Sound and Vibration, 2006, 294(1-2):269-277
[32]P.D. MCFADDEN, M.M. TOOZHY. Application of synchronous averaging to vibration monitoring of rolling element bearings[J]. Mechanical Systems and Signal Processing, 2000, 14(6):891-906
[33]Qiuhua Du, Shunian Yang. Application of the EMD method in the vibration analysis of ball bearings[J]. Mechanical Systems and Signal Processing, 2007,21(6):2634-2644
[34]赵玉成,陈荣华,马占国.旋转机械动力辨识与故障诊断技术[M].徐州：中国矿业大学出版社,2008：105-106
[35]杨淑莹.模式识别与智能计算-Matlab技术实现[M].北京：电子工业出版社,2008：141-147
[36]阎平凡,张长水.人工神经网络与模拟进化计算[M].北京：清华大学出版社,2000：3-4
[37]飞思科技产品研发中心.神经网络理论与MATLAB7实现[M].北京：电子工业出版社,2005：22-23
[38]Matlab中文论坛.Matlab神经网络30个案例分析[M].北京：北京航空航天大学出版社,2010：1-2
[39]周瑞峰.滚动轴承故障智能诊断方法的研究与实现[硕士学位论文].大连.大连理工大学.2008
[40]褚福磊,彭志科,冯志鹏等.机械故障诊断中的现代信号处理方法[M].北京：科学出版社,2009：34-35
[2]盛兆顺、尹琦岭.设备状态监测与故障诊断技术及应用[M].北京：化学工业出版社,2003：224-225
[3]曾刚.滚动轴承微弱故障信号检测方法研究[硕士学位论文].成都.成都理工大学.2010
[4]梅宏斌.滚动轴承振动监测与诊断[M].北京：机械工业出版社,1995：1-10
[5]苏文胜.滚动轴承振动信号处理及特征提取方法研究[博士学位论文].大连.大连理工大学.2010
[6]葛哲学,陈仲生MATLAB时频分析技术及其应用[M].北京：人民邮电出版社,2006：2-5
[7]屈梁生,张西宁,沈玉娣.机械故障诊断理论与方法[M].西安：西安交通大学出版社,2009：117-118
[8]李舜酩,李香莲.振动信号的现代分析技术与应用[M].北京：国防工业出版社,2008：57-59
[9]Norden E. Huang, Zheng Shen, Steven R. Long, et al.The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis. Proc.Roy.Soc. Lond.1998.454 (A):903-993
[10]Jon S. Wilson.林龙信,邓彬,张鼎等译.传感器技术手册[M].北京：人民邮电出版社,2009：222-224
[11]SKF, Vibration Diagnostic Guide, http://www.skf.com/portal/skf/home/aptitudexchange? contentId=0.237932.237933.237934.237960.867870, 2009
[12]张德丰.Matlab小波分析[M].北京：机械工业出版社,2009：50-51
[13]彭志科.小波分析在旋转设备故障诊断中的应用[博士学位论文].北京.清华大学.2002
[14]潘泉,张磊,孟晋丽等.小波滤波方法及应用[M].北京：清华大学出版社,2005：6-7
[15]蒋永华.旋转机械非平稳信号微弱特征提取方法研究[博士学位论文].重庆.重庆大学.2010
[16]胡昌华,张军波,夏军等.基于MATLAB的系统分析与设计-小波分析[M].西安：西安电子科技大学出版社,1999：5-6
[17]Norden E. Huang, Samuel S P Shen. Hilbert-Huang Transform and Its Applications. Hackensack. World Scientific Publishing [M].2005:1-2
[18]Norden E. Huang, Nii O. Attoh-Okine. The Hilbert-Huang Transform in Engineering[M]. Boca Raton. CRC Press.2005:3-4
[19]Gabriel Rilling, Patrick Flandrin, Paulo Goncalves. On Empirical Mode Decomposition and Its Algorithms. Proc. IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing, 2003:8-11
[20]黄大吉,赵进平,苏纪兰.希尔伯特-黄变换的端点延拓[J].海洋学报.2003,25(1)：1-11
[21]Yu Yang, Dejie Yu, Junsheng Cheng. A fault diagnosis approach for roller bearing based on IMF envelope spectrum and SVM[J]. Measurement, 2007,40(9-10):943-950
[22]Z.PENG, F.CHU, Y.HE. Vibration signal analysis and feature extraction based on reassigned wavelet scalogram [J]. Journal of Sound and Vibration, 2002, 253(5):1087-1100
[23]周福昌,陈进,何俊等.循环平稳信号处理在机械设备故障诊断中的应用综述[J].振动 与冲击.2006,25(5)：148-152
[24]G.F. Bin, J.J. Gao, X.J. Li, et al. Early fault diagnosis of rotating machinery based on wavelet packets-Empirical mode decomposition feature extraction and neural network [J]. Mechanical Systems and Signal Processing, 2012,27:696-711
[25]Yuping Zhang, Shufeng Ai. EMD based envelope analysis for bearing faults detection [A]. In: 7th World Congress on Intelligent Control and Automation [C].2008:4257-4260
[26]韩捷,张瑞林.旋转机械故障机理及诊断技术[M].北京：机械工业出版社,1997：37-38
[27]胡婧.基于BP神经网络的滚动轴承缺陷诊断研究[硕士学位论文].武汉.华中科技大学.2006
[28]于德介,程军圣,杨宇.机械故障诊断的Hilbert-Huang变换方法[M].北京：科学出版社,2006：154-155
[29]程军圣.基于Hilbert-Huang变换的旋转机械故障诊断方法研究[博士学位论文].长沙.湖南大学.2005
[30]杨勇.EMD和模糊神经网络在滚动轴承故障诊断中的研究与应用[硕士学位论文].太原.太原理工大学.2008
[31]Yang Yu, Yu Dejie, Cheng Junsheng. A roller bearing fault diagnosis method based on EMD energy entropy and ANN [J]. Journal of Sound and Vibration, 2006, 294(1-2):269-277
[32]P.D. MCFADDEN, M.M. TOOZHY. Application of synchronous averaging to vibration monitoring of rolling element bearings[J]. Mechanical Systems and Signal Processing, 2000, 14(6):891-906
[33]Qiuhua Du, Shunian Yang. Application of the EMD method in the vibration analysis of ball bearings[J]. Mechanical Systems and Signal Processing, 2007,21(6):2634-2644
[34]赵玉成,陈荣华,马占国.旋转机械动力辨识与故障诊断技术[M].徐州：中国矿业大学出版社,2008：105-106
[35]杨淑莹.模式识别与智能计算-Matlab技术实现[M].北京：电子工业出版社,2008：141-147
[36]阎平凡,张长水.人工神经网络与模拟进化计算[M].北京：清华大学出版社,2000：3-4
[37]飞思科技产品研发中心.神经网络理论与MATLAB7实现[M].北京：电子工业出版社,2005：22-23
[38]Matlab中文论坛.Matlab神经网络30个案例分析[M].北京：北京航空航天大学出版社,2010：1-2
[39]周瑞峰.滚动轴承故障智能诊断方法的研究与实现[硕士学位论文].大连.大连理工大学.2008
[40]褚福磊,彭志科,冯志鹏等.机械故障诊断中的现代信号处理方法[M].北京：科学出版社,2009：34-35
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