轨道车辆健康诊断系统的研究
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摘要
当今随着中国轨道车辆的发展,轨道车辆成为人们日常出行必不可缺的交通工具。人们对轨道车辆的依赖,使着人们对轨道车辆的运行安全性提出了更高的要求。轨道车辆传动系统的机械故障便成了我们研究的话题,轴箱、齿轮箱和电机是轨道车辆传动系统中有可能会引起故障的关键部件,对轴箱、齿轮箱和电机能够有效地进行故障监测和健康诊断有着实际的意义。本文通过对轨道冲击、车轮不圆、轴箱轴承故障、齿轮箱故障、电机故障、联轴节故障及关键部件的固有模态频率分别进行诊断分析,建立强大而丰富的特征频率故障数据库,给出轨道车辆健康诊断系统的概念,分析振动传递的相关路径。故障分析方法有很多种,而本文主要采用一种新的非线性非平稳信号分析方法——Hilbert-Huang变换。在传统傅立叶变换的基础上,本文首先主要研究了特征故障频率在轴箱、齿轮箱、电机及构架上的表现特征,发现轴箱、齿轮箱和电机的故障特征频率一般在构架上都有比较明显的表现；通过Hilbert-Huang变换更加明确地验证了在传统傅立叶变换方法中说明的现象,而且能够准确明显地提取重要的故障特征频率。
目前,对于轨道车辆整个传动系统进行系统地故障诊断的工作还是比较少的,对于轨道车辆的故障判断与识别没有一个统一的标准。当今高速铁路快速发展,轨道车辆随时都有可能出现故障,而面对突如其来的故障,工作人员无法找到故障类型和部位,本文将提供一个相对比较完整的故障特征数据库,在今后的故障诊断中,能够为工作人员进行故障分析时提供一点小小的帮助。对于本论文来说,如果它能够很好地对传动系统机械故障诊断做一个比较全面和系统的分析,那将是轨道车辆故障诊断方面崭新的一页。
Nowadays with the development of China'railway vehicles, rail vehicles become the essential transportation tool of people daily travel. Because people rely on railway vehicles, so the safety of the running railway vehicles is put forward higher request. The mechanical fault of Railway vehicle transmission system becomes our research topic. Axle box, gearbox and motor may be likely to cause failure as the key parts of drive system for the railway vehicles, it is practical significance that axle box, gearbox and motor can be fault monitoring and health diagnosis effectively. This paper analysis the rail impact, not round wheel, axle box bearing failure, failure of gear box, motor, coupling fault diagnose and inherent modal frequencies of the key components, and establish a strong and rich characteristic frequency fault database, the concept of health diagnosis system for rail vehicle is given in this paper, analysis the vibration transfer path. There are many kinds of failure analysis method, and this paper mainly adopts a new method of nonlinear and non-stationary signal analysis, it is Hilbert Huang transform. On the basis of the traditional Fourier transform, this paper mainly studies that the characteristic fault frequency is performance characteristics on the axle box, gearbox and motor, and find that the fault characteristic frequency of axle box, gear box and motor generally has obvious performance on the bogie structure. It is clearly true that the description of phenomenon in the traditional Fourier transform method can be verified, but also the important fault characteristic frequency can be accurately extracted by the Hilbert Huang transform.
At present, the fault diagnosis work for the whole transmission system of railway vehicles is a few. The fault diagnosis and recognition for railway vehicle does not have a unified standard. In today's rapid development of high-speed railway, railway vehicles are likely to fail at any time, and in the face of sudden failure, workers can't find fault type and area, this paper will provide a relatively complete database, hoping that the database can provide a little help to workers when they do the fault analysis and diagnosis in the future. For this paper, if it can do more comprehensive and systematic analysis for the fault diagnosis of the mechanical transmission system, it will be a new page about the fault diagnosis of railway vehicle.
目前,对于轨道车辆整个传动系统进行系统地故障诊断的工作还是比较少的,对于轨道车辆的故障判断与识别没有一个统一的标准。当今高速铁路快速发展,轨道车辆随时都有可能出现故障,而面对突如其来的故障,工作人员无法找到故障类型和部位,本文将提供一个相对比较完整的故障特征数据库,在今后的故障诊断中,能够为工作人员进行故障分析时提供一点小小的帮助。对于本论文来说,如果它能够很好地对传动系统机械故障诊断做一个比较全面和系统的分析,那将是轨道车辆故障诊断方面崭新的一页。
Nowadays with the development of China'railway vehicles, rail vehicles become the essential transportation tool of people daily travel. Because people rely on railway vehicles, so the safety of the running railway vehicles is put forward higher request. The mechanical fault of Railway vehicle transmission system becomes our research topic. Axle box, gearbox and motor may be likely to cause failure as the key parts of drive system for the railway vehicles, it is practical significance that axle box, gearbox and motor can be fault monitoring and health diagnosis effectively. This paper analysis the rail impact, not round wheel, axle box bearing failure, failure of gear box, motor, coupling fault diagnose and inherent modal frequencies of the key components, and establish a strong and rich characteristic frequency fault database, the concept of health diagnosis system for rail vehicle is given in this paper, analysis the vibration transfer path. There are many kinds of failure analysis method, and this paper mainly adopts a new method of nonlinear and non-stationary signal analysis, it is Hilbert Huang transform. On the basis of the traditional Fourier transform, this paper mainly studies that the characteristic fault frequency is performance characteristics on the axle box, gearbox and motor, and find that the fault characteristic frequency of axle box, gear box and motor generally has obvious performance on the bogie structure. It is clearly true that the description of phenomenon in the traditional Fourier transform method can be verified, but also the important fault characteristic frequency can be accurately extracted by the Hilbert Huang transform.
At present, the fault diagnosis work for the whole transmission system of railway vehicles is a few. The fault diagnosis and recognition for railway vehicle does not have a unified standard. In today's rapid development of high-speed railway, railway vehicles are likely to fail at any time, and in the face of sudden failure, workers can't find fault type and area, this paper will provide a relatively complete database, hoping that the database can provide a little help to workers when they do the fault analysis and diagnosis in the future. For this paper, if it can do more comprehensive and systematic analysis for the fault diagnosis of the mechanical transmission system, it will be a new page about the fault diagnosis of railway vehicle.
引文
[1]黄文虎,夏松波.设备故障原理、技术及应用[M]北京：科技出版社,1996.
[2]屈梁生,何正嘉.机械故障诊断学[M]上海：上海科学技术出版社,1986.
[3]丁康,李巍华,朱小勇.齿轮及齿轮(?)故障诊断实用技术[M]北京：机械工业出版社,2005
[4]张贤达,保铮.非平稳信号分析处理[M]北京：国防工业出版社,1998.
[5]刘林.时频分析理论和应用[J].计算机自动测量与控制,2001,9(4)：44-47
[6]Huang N E, Shen Z, Long S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis [J]. Proceedings of the Royal Society London. A,1998,454,903-995.
[7]N.E.Huang, M.Wu, S.Long et al. A confidence limit for the empirical mode decomposition and Hilbert spectral analysis [J]. Proc.Royal Soc.London.A,2003,459(2307): 2317-2345.
[8]N.E.Huang, Z.Shen, S.Long. A new view of nonlinear water waves:The Hilbert Spectrum[J].Annu.Rev.Fluid Mech.1999,31(1):417-457.
[9]G Rilling, P.Flandrin. On empirical mode decomposition and its algorithms[J].IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing NSIP-03 Grado(1), June 2003
[10]R.Deering and J.F.Kaiser. The use of a masking signal to improve empirical mode decomposition[J].ICASSP 2005.
[11]邓拥军,王伟.EMD方法及Hilbert变换中边界问题的处理[J].科学通报,2001,46(3)：257-260
[12]钟佑明,秦树人.Hilbert-Huang变换中的理论研究[J].振动与冲击,2002,21(4)：14-17
[13]黄大吉,赵进平.希尔伯特—黄变换的端点延拓[J].海洋学报,2003,25(1)：1-11
[14]胡劲松,杨世锡.基于HHT的旋转机械故障诊断方法研究[J].动力工程,2004,24(6)：845-851
[15]陈忠,郑时雄.基于经验模式分解(EMD)的齿轮箱齿轮故障诊断技术研究[J].振动 工程学报.2003,16(2)：229-232
[16]高强,杜小山,范虹等.滚动轴承故障的EMD诊断方法研究[J].振动工程学报.2007,20(1)：15-18
[17]丁康,孔正国.振动调幅调频信号的调制边频带分析及其解调方法[J].振动与冲击,2005.24(6)：9-12
[18]康海英,栾军英.基于时频和频谱分析的齿轮箱故障诊断[J].军械工程学院学报,2004,6,16(3).10-13
[19]沈标正.电机故障诊断技术(第一版).北京：机械工业出版社,1996：1-578
[20]陈继清.基于Hilbert-Huang变换的齿轮箱故障诊断[D].硕士学位论文
[21]胡劲松,杨世锡,吴昭同等.旋转机械振动信号的HHT和STFT时频谱图比较研究[J].汽轮机技术,2002,44(6)：336-338
[22]范德功.基于希尔伯特黄变换机械故障诊断的研究[D].北京：华北电力大学,2006：54-56
[23]邓拥军,王伟,钱成春等.EMD方法及Hilbert变换中边界问题的处理[J].科学通报,2001,46(3)：257-263
[24]程家兴.利用Hilbert变换提取信号瞬时特征参数的问题研究[J].电讯技术,2003,14(4)：45-47
[25]黄伟国.基于振动信号特征提取与表达的旋转机械状态监测与故障诊断研究[D].合肥：中国科学技术大学,2010
[26]毕果,陈进,何俊,李富才,周福昌.齿轮信号特征识别的谱相关密度分析[J].上海交通大学学报,2006,40(7)：1084-1087
[27]Gao, R.X. and Yan, R.. Non-stationary signal processing for bearing health monitoring[J]. International J. Manufacturing Research,2006,1 (1):18-40
[28]Cohen, L. Time-Frequency Analysis[M]. Prentice-Hall, Englewood Cliffs, NJ,1995
[29]Antoni, J. and Randall, Randall, R.B.. The spectral kurtosis:application to the vibratory surveillance and diagnostics of rotating machines[J]. Mechanical Systems and Signal Processing,2006,20 (2):308-331.
[30]Antoni, J.. The spectral kurtosis:a useful tool for characterizing non-stationary signals[J]. Mechanical Systems and Signal Processing,2006,20 (2):282-307.
[31]姜鸣,陈进.循环自相关函数的解调性能分析[J].上海交通大学学报,2002,36(06)：799-802
[32]贾民平,杨建文.滚动轴承振动的周期平稳性分析及故障诊断[J].机械工程学报,2007,43(1)：144-146.
[33]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.
[34]何晓霞,沈玉娣,张西宁.连续小波变换在滚动轴承故障诊断中的应用[J].机械科学与技术,2001,20(4)：571-574.
[35]程军圣,于德介,杨宇等.尺度-小波能量谱在滚动轴承故障诊断中的应用[J].振动工程学报,2004,17(1)：82-85.
[36]刘红星,陈涛,屈梁生等.能量算子解调方法及其在机械信号解调中的应用lJ].机械工程学报,1998,34(5)：85-90.
[37]丁康,李魏华,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.
[38]丁康,王延春.传动箱齿轮和轴故障的振动诊断方法的研究[J].振动与冲击,1994,10(2)：235-240.
[39]秦恺,陈进,姜鸣等.一种滚动轴承故障特征提取的新方法——谱相关密度[J].振动与冲击,2001,20(01)：34-37.
[40]翟婉明.高速铁路轮轨冲击振动的特征及其控制原理.铁道学报.1995,17(3)：28-33.
[41]罗仁,曾京,邬平波,戴焕云.高速列车车轮不圆顺磨耗仿真及分析.铁道学报.2010,32(5)：30-35.
[42]陈光雄,金学松,邬平波,戴焕云,周仲荣.车轮多边形磨耗机理的有限元研究.铁道学报.2011,33(1)：14-18.
[43]W.Zhai,X.Sun. A detailed model for investigating vertical interactions between railway vehicle and track. Vehicle System Dynamics.1994,Vol.23,supplement:603-615.
[44]王福天.车辆系统动力学.北京：中国铁道出版社,1994.
[45]冯自立.CRH2型动车组轴箱轴承常见表面损伤原因分析[J].机车车艺,2010,6(3)：40-41
[46]萨师煊,王珊数.数据库系统概论[M].北京：高等教育出版社.2000
[47]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
[2]屈梁生,何正嘉.机械故障诊断学[M]上海：上海科学技术出版社,1986.
[3]丁康,李巍华,朱小勇.齿轮及齿轮(?)故障诊断实用技术[M]北京：机械工业出版社,2005
[4]张贤达,保铮.非平稳信号分析处理[M]北京：国防工业出版社,1998.
[5]刘林.时频分析理论和应用[J].计算机自动测量与控制,2001,9(4)：44-47
[6]Huang N E, Shen Z, Long S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis [J]. Proceedings of the Royal Society London. A,1998,454,903-995.
[7]N.E.Huang, M.Wu, S.Long et al. A confidence limit for the empirical mode decomposition and Hilbert spectral analysis [J]. Proc.Royal Soc.London.A,2003,459(2307): 2317-2345.
[8]N.E.Huang, Z.Shen, S.Long. A new view of nonlinear water waves:The Hilbert Spectrum[J].Annu.Rev.Fluid Mech.1999,31(1):417-457.
[9]G Rilling, P.Flandrin. On empirical mode decomposition and its algorithms[J].IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing NSIP-03 Grado(1), June 2003
[10]R.Deering and J.F.Kaiser. The use of a masking signal to improve empirical mode decomposition[J].ICASSP 2005.
[11]邓拥军,王伟.EMD方法及Hilbert变换中边界问题的处理[J].科学通报,2001,46(3)：257-260
[12]钟佑明,秦树人.Hilbert-Huang变换中的理论研究[J].振动与冲击,2002,21(4)：14-17
[13]黄大吉,赵进平.希尔伯特—黄变换的端点延拓[J].海洋学报,2003,25(1)：1-11
[14]胡劲松,杨世锡.基于HHT的旋转机械故障诊断方法研究[J].动力工程,2004,24(6)：845-851
[15]陈忠,郑时雄.基于经验模式分解(EMD)的齿轮箱齿轮故障诊断技术研究[J].振动 工程学报.2003,16(2)：229-232
[16]高强,杜小山,范虹等.滚动轴承故障的EMD诊断方法研究[J].振动工程学报.2007,20(1)：15-18
[17]丁康,孔正国.振动调幅调频信号的调制边频带分析及其解调方法[J].振动与冲击,2005.24(6)：9-12
[18]康海英,栾军英.基于时频和频谱分析的齿轮箱故障诊断[J].军械工程学院学报,2004,6,16(3).10-13
[19]沈标正.电机故障诊断技术(第一版).北京：机械工业出版社,1996：1-578
[20]陈继清.基于Hilbert-Huang变换的齿轮箱故障诊断[D].硕士学位论文
[21]胡劲松,杨世锡,吴昭同等.旋转机械振动信号的HHT和STFT时频谱图比较研究[J].汽轮机技术,2002,44(6)：336-338
[22]范德功.基于希尔伯特黄变换机械故障诊断的研究[D].北京：华北电力大学,2006：54-56
[23]邓拥军,王伟,钱成春等.EMD方法及Hilbert变换中边界问题的处理[J].科学通报,2001,46(3)：257-263
[24]程家兴.利用Hilbert变换提取信号瞬时特征参数的问题研究[J].电讯技术,2003,14(4)：45-47
[25]黄伟国.基于振动信号特征提取与表达的旋转机械状态监测与故障诊断研究[D].合肥：中国科学技术大学,2010
[26]毕果,陈进,何俊,李富才,周福昌.齿轮信号特征识别的谱相关密度分析[J].上海交通大学学报,2006,40(7)：1084-1087
[27]Gao, R.X. and Yan, R.. Non-stationary signal processing for bearing health monitoring[J]. International J. Manufacturing Research,2006,1 (1):18-40
[28]Cohen, L. Time-Frequency Analysis[M]. Prentice-Hall, Englewood Cliffs, NJ,1995
[29]Antoni, J. and Randall, Randall, R.B.. The spectral kurtosis:application to the vibratory surveillance and diagnostics of rotating machines[J]. Mechanical Systems and Signal Processing,2006,20 (2):308-331.
[30]Antoni, J.. The spectral kurtosis:a useful tool for characterizing non-stationary signals[J]. Mechanical Systems and Signal Processing,2006,20 (2):282-307.
[31]姜鸣,陈进.循环自相关函数的解调性能分析[J].上海交通大学学报,2002,36(06)：799-802
[32]贾民平,杨建文.滚动轴承振动的周期平稳性分析及故障诊断[J].机械工程学报,2007,43(1)：144-146.
[33]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.
[34]何晓霞,沈玉娣,张西宁.连续小波变换在滚动轴承故障诊断中的应用[J].机械科学与技术,2001,20(4)：571-574.
[35]程军圣,于德介,杨宇等.尺度-小波能量谱在滚动轴承故障诊断中的应用[J].振动工程学报,2004,17(1)：82-85.
[36]刘红星,陈涛,屈梁生等.能量算子解调方法及其在机械信号解调中的应用lJ].机械工程学报,1998,34(5)：85-90.
[37]丁康,李魏华,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.
[38]丁康,王延春.传动箱齿轮和轴故障的振动诊断方法的研究[J].振动与冲击,1994,10(2)：235-240.
[39]秦恺,陈进,姜鸣等.一种滚动轴承故障特征提取的新方法——谱相关密度[J].振动与冲击,2001,20(01)：34-37.
[40]翟婉明.高速铁路轮轨冲击振动的特征及其控制原理.铁道学报.1995,17(3)：28-33.
[41]罗仁,曾京,邬平波,戴焕云.高速列车车轮不圆顺磨耗仿真及分析.铁道学报.2010,32(5)：30-35.
[42]陈光雄,金学松,邬平波,戴焕云,周仲荣.车轮多边形磨耗机理的有限元研究.铁道学报.2011,33(1)：14-18.
[43]W.Zhai,X.Sun. A detailed model for investigating vertical interactions between railway vehicle and track. Vehicle System Dynamics.1994,Vol.23,supplement:603-615.
[44]王福天.车辆系统动力学.北京：中国铁道出版社,1994.
[45]冯自立.CRH2型动车组轴箱轴承常见表面损伤原因分析[J].机车车艺,2010,6(3)：40-41
[46]萨师煊,王珊数.数据库系统概论[M].北京：高等教育出版社.2000
[47]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