列车轮对故障振动特性及诊断关键技术研究
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摘要
铁路是我国交通运输的重要基础设施,而列车是铁路运输的具体载运工具。列车轮对作为机车车辆的支撑走行部件,由于其长期处于高速重载多粉尘的工作环境,相关工作面长期承受交变接触应力的作用,极易引起零件的疲劳和裂纹等早期缺陷。如任由缺陷继续发展,会给列车带来额外的冲击振动,导致零件发热和车轴裂纹,继而产生燃轴切轴,甚至导致车毁人亡的严重事故。因此,在列车运行过程中对轮对关键部件开展状态监测与故障诊断,及时发现早期故障是非常有必要的。
本论文通过理论分析提出频带变化类故障的定义。在详细分析列车轮对关键部件结构和振动特性的基础上,开展了牵引齿轮、机车轴箱轴承和电机轴承的故障振动特性、频带变化故障机理以及监测方法和故障诊断信号处理算法的研究。通过理论分析、仿真研究、实验分析以及实际应用相结合的方法开展研究。主要研究内容如下：
(1)分析了列车轮对关键部件的机械结构和特点,分别对其振动特性和故障机理等进行了研究,归纳了列车轮对常见故障的频谱特性。详细分析了列车运行过程中所受到的外部影响因素,在此基础上归纳并提出了频带变化类影响因素的概念,提出了列车轮对频带变化类故障定义。并建立该类故障的通用数学模型。
(2)定义与列车运行状态因素无关的故障特征参数K。分析故障特征频率fg与故障特征参数K之间的相互关系,将常规的频谱分析方法归一化应用到特征域信号中,实现特征域信号分析方法。并详细分析了特征域分析的原理,研究了特征域分析实现的若干关键技术(整周期采样电路的设计、采样参数的选取、等角度重采样分析技术等)。并通过故障信号的仿真分析和列车轮对故障实验平台的实验研究验证了特征域分析方法的准确性和实用性。仿真分析和实验结果表明,该方法可以实现列车轮对故障的无转速波动敏感性的精确诊断。
(3)分析了列车轮对振动信号的似周期特性。研究了列车轮对频带变化类故障的自相关循环平稳特性。选取谱相关密度函数作为列车振动信号的循环统计量进行研究。通过列车轮对振动信号的循环自相关函数,对其降噪效果进行了研究。以列车运行过程中最常见的加性噪声为例,分析了循环自相关函数的降噪效果。继而研究了谱相关密度函数对列车轮对振动信号中常见噪声分量的降噪特性。通过仿真信号和实验数据加以验证,取得了较为理想的效果。
(4)选取信号循环平稳特性分析中的循环频率α加以分析,提出了基于全频段扫频算法的循环频率α提取算法。通过计算循环频率α处的谱相关密度函数,提取列车轮对频带变化类故障特征。并研究了高阶谱分析方法在轮对故障诊断中的应用。结合等角度信号采集技术,提出了等角度信号的双谱分析方法,通过双谱对角切片谱分析方法,提取故障特征信息。实际应用研究表明该方法具有一定的实用性。
(5)研究了列车轮对故障的局域均值分解方法(简记为LMD),结合列车轮对故障信号的特点,采用LMD方法对信号加以分解。提出了窗口滑动平均处理的LMD技术,将多分量信号分解为若干单分量信号,提取列车轮对转频瞬时频率,以实现无转速跟踪的特征域信号分析技术。对于LMD的端点效应,提出基于原始信号局域波形统计特征的延拓特征波方法,以消除分解算法的端点效应。定义端点效应烈度公式,定量分析消除效果。通过应用分析,验证了上述方法。
(6)采用信号时频分析方法提取其时频特征。提出了列车轮对状态实时监测的修正多项式WVD。研究了考虑频带变化因素的时核函数改进方法。针对多项式核函数的WVD分布图较为复杂,提出了基于Viterbi算法的时频分布图最优路径搜索算法。通过列车轮对故障实验平台和实际列车运行数据分析加以验证。
论文中针对列车轮对监测与故障诊断所提出的各种方法均经过了严格的理论分析、模拟实验或实际运行实验,具有较强的实际工程应用性。且论文提出的相关分析方法,是在综合考虑列车运行过程中普遍存在的转速波动情况,提出精确诊断算法。相关的监测诊断方法,不仅适用于列车轮对的监测诊断,还可推广应用到其他存在转速波动的旋转机械监测诊断中,具有非常明显的是实际应用意义。
Railway is the important infrastructure of transportation, and the train is the specific carrying vehicle of rail transport. The operation condition of train is a key factor to the rail transport safety. Therefore, the safety operation is a top priority to protect human life and property security, even to the economic development which has a very important practical significance.
As the wheel-set components are the supporting parts and traveling parts of the train, due to its poor operation condition such as long period of high-speed and heavy loads situation, the contact surface of the relevant rolling components is always attacked by the long-term alternating stress. The rolling components are easily caused to fatigue, crack or other faults. If such faults are continued developing will bring additional shock and vibration to the train, and lead to bearing heating, axis cutting, and even lead to a serious crash accident. It's necessary to apply condition monitoring and fault diagnosis methods to the key components of wheel set, detect the early fault and avoid the accident which is dangerous to the railway transportation.
This paper focuses on the train wheel-set's early defects of during operation, by theoretical analysis the definition of the frequency-varying fault is put forward. The fault mechanism, condition monitoring methods, fault signal processing algorithms of train wheel-set's frequency-varying fault are researched based on the detailed analysis of the key components structure and kinetic characteristics of traction gear fault, axle box bearing fault and the motor shaft bearing fault. By theoretical analysis, simulation studies, laboratory analysis and actual applications, a combination of monitoring and diagnosis method is systematic studied. The main research content and results are as follows:
(1) The structure and mechanical characteristics of the train wheel set component is analyses in this part. The vibration characteristics, fault mechanism and fault spectrum characteristics is researched. The common fault frequency characteristics of the train wheel set component are summarized. Through the detail analysis of the external influences which is different from the common rotating machinery, the concept of frequency-varying factors is summarized and presented. By entirely think of the operation factors, the general fault mechanism model is established.
(2) By the definition of the fault characteristic coefficient K which is unrelated to the frequency-varying factors, the conventional spectral analysis method can be normalized to the characteristic domain signal, and the characteristics spectral analysis method is achieved. The principle and some key technologies (such as:synchronous sampling design, sampling parameters selected et al.) of characteristic spectral analysis method is analysed in detail. Through simulation analysis and experimental study on the train wheel set fault test system, the accuracy and usefulness of characteristics spectral analysis method is verified.
(3) In this part, the train likely-cycle vibration signal is analysised. By research the cyclostationary properties of the frequency-varying fault signal, the spectral correlation density function analysis method of train wheel set vibration signal's cycle statistics parmeter is selected. From the circulating the auto-correlation function of train wheel set vibration signals, the noise reduction analysis is proposed. The noise characteristic of the train wheel set vibration signal is researched through spectral correlation density function based on the auto-correlation function analysis of additive noise. And the noise reduction effect is verified by the simulated signals and experimental data analysis.
(4) The cyclic statistics method of is applied to extraction the fault characteristic of the frequency-varying fault. For calculating the cyclostationary properties as cycle frequency α, a cycle frequency a calculate algorithm based on the full frequency band sweep extraction method is proposed. By calculate the spectral correlation density function value where the cycle frequency a is get from the frequency band sweep method. The fault characteristic is extracted from the wheel components to achieve a precise diagnosis for such fault. In the last of this part, a higher order bispectrum analysis is applied to the train wheel set fault. Be combined with the characteristic domain signal processing method proposed in this paper. The characteristic domain's higher order bispectrum is put forward. And the characteristic domain bispectrum diagonal slice is used to extract fault characteristic. Through the practical application research, it shows that such analysis method has a certain practicality.
(5) The train wheel set frequency-varying fault signal's local mean decomposition method (denoted by LMD) is studied in this part. Full account of the multi-component AM-FM signal collect from train wheel set fault, the local mean decomposition method is applied to decompose this signal. The new local mean decomposition method based on the average processing with window sliding extraction technology is proposed and will be applied to extract a multi-component signal into several single-component signals. In order to achieve the no speed-tracking characteristic domain signal analysis techniques, the train wheel set instantaneous frequency of rotating frequency is extract from the several signal-component signals. By analyzing the principles and causes of the end effect in the local mean decomposition, a extension characteristic wave method based on the statistical characteristic of the original signal ending localized waveform is proposed. In order to eliminate the end effect of the decomposition algorithm, an intensity formula of the end effect's quantitative analysis is defined. All the method put forwarded above is verified in the real case application, and achieved satisfactory results.
(6) For the non-stationary and non-linear characteristics of the fault signal, a time-frequency analysis method is researched in order to extract the fault signal's time-frequency characteristic. A variables improved method of the kernel function is researched. And an amendments polynomial Wigner-Ville distribution for the train wheel real-time status monitoring is proposed. For the polynomial kernel function of the signal frequency distribution map is more complicated, a frequency map optimal path search algorithm based on the Viterbi algorithm is proposed. Through the experiment on the train wheel set fault test system and the actual train running data analysis, the methods researched above is verified, and obtain a satisfactory result.
The fault diagnosis analysis methods which are proposed in this paper are all undergoing by a rigorous theoretical analysis, simulation and real experiment has a strong practical application of engineering. This paper presents analytical methods is account of the frequency-varying factors, the precise diagnostic algorithm is put forward and verified. Such monitoring and diagnostic methods and techniques are not only can used to the train wheel set components, but also can promote the use of any possible speed fluctuations rotating machinery monitoring and diagnosis.
本论文通过理论分析提出频带变化类故障的定义。在详细分析列车轮对关键部件结构和振动特性的基础上,开展了牵引齿轮、机车轴箱轴承和电机轴承的故障振动特性、频带变化故障机理以及监测方法和故障诊断信号处理算法的研究。通过理论分析、仿真研究、实验分析以及实际应用相结合的方法开展研究。主要研究内容如下：
(1)分析了列车轮对关键部件的机械结构和特点,分别对其振动特性和故障机理等进行了研究,归纳了列车轮对常见故障的频谱特性。详细分析了列车运行过程中所受到的外部影响因素,在此基础上归纳并提出了频带变化类影响因素的概念,提出了列车轮对频带变化类故障定义。并建立该类故障的通用数学模型。
(2)定义与列车运行状态因素无关的故障特征参数K。分析故障特征频率fg与故障特征参数K之间的相互关系,将常规的频谱分析方法归一化应用到特征域信号中,实现特征域信号分析方法。并详细分析了特征域分析的原理,研究了特征域分析实现的若干关键技术(整周期采样电路的设计、采样参数的选取、等角度重采样分析技术等)。并通过故障信号的仿真分析和列车轮对故障实验平台的实验研究验证了特征域分析方法的准确性和实用性。仿真分析和实验结果表明,该方法可以实现列车轮对故障的无转速波动敏感性的精确诊断。
(3)分析了列车轮对振动信号的似周期特性。研究了列车轮对频带变化类故障的自相关循环平稳特性。选取谱相关密度函数作为列车振动信号的循环统计量进行研究。通过列车轮对振动信号的循环自相关函数,对其降噪效果进行了研究。以列车运行过程中最常见的加性噪声为例,分析了循环自相关函数的降噪效果。继而研究了谱相关密度函数对列车轮对振动信号中常见噪声分量的降噪特性。通过仿真信号和实验数据加以验证,取得了较为理想的效果。
(4)选取信号循环平稳特性分析中的循环频率α加以分析,提出了基于全频段扫频算法的循环频率α提取算法。通过计算循环频率α处的谱相关密度函数,提取列车轮对频带变化类故障特征。并研究了高阶谱分析方法在轮对故障诊断中的应用。结合等角度信号采集技术,提出了等角度信号的双谱分析方法,通过双谱对角切片谱分析方法,提取故障特征信息。实际应用研究表明该方法具有一定的实用性。
(5)研究了列车轮对故障的局域均值分解方法(简记为LMD),结合列车轮对故障信号的特点,采用LMD方法对信号加以分解。提出了窗口滑动平均处理的LMD技术,将多分量信号分解为若干单分量信号,提取列车轮对转频瞬时频率,以实现无转速跟踪的特征域信号分析技术。对于LMD的端点效应,提出基于原始信号局域波形统计特征的延拓特征波方法,以消除分解算法的端点效应。定义端点效应烈度公式,定量分析消除效果。通过应用分析,验证了上述方法。
(6)采用信号时频分析方法提取其时频特征。提出了列车轮对状态实时监测的修正多项式WVD。研究了考虑频带变化因素的时核函数改进方法。针对多项式核函数的WVD分布图较为复杂,提出了基于Viterbi算法的时频分布图最优路径搜索算法。通过列车轮对故障实验平台和实际列车运行数据分析加以验证。
论文中针对列车轮对监测与故障诊断所提出的各种方法均经过了严格的理论分析、模拟实验或实际运行实验,具有较强的实际工程应用性。且论文提出的相关分析方法,是在综合考虑列车运行过程中普遍存在的转速波动情况,提出精确诊断算法。相关的监测诊断方法,不仅适用于列车轮对的监测诊断,还可推广应用到其他存在转速波动的旋转机械监测诊断中,具有非常明显的是实际应用意义。
Railway is the important infrastructure of transportation, and the train is the specific carrying vehicle of rail transport. The operation condition of train is a key factor to the rail transport safety. Therefore, the safety operation is a top priority to protect human life and property security, even to the economic development which has a very important practical significance.
As the wheel-set components are the supporting parts and traveling parts of the train, due to its poor operation condition such as long period of high-speed and heavy loads situation, the contact surface of the relevant rolling components is always attacked by the long-term alternating stress. The rolling components are easily caused to fatigue, crack or other faults. If such faults are continued developing will bring additional shock and vibration to the train, and lead to bearing heating, axis cutting, and even lead to a serious crash accident. It's necessary to apply condition monitoring and fault diagnosis methods to the key components of wheel set, detect the early fault and avoid the accident which is dangerous to the railway transportation.
This paper focuses on the train wheel-set's early defects of during operation, by theoretical analysis the definition of the frequency-varying fault is put forward. The fault mechanism, condition monitoring methods, fault signal processing algorithms of train wheel-set's frequency-varying fault are researched based on the detailed analysis of the key components structure and kinetic characteristics of traction gear fault, axle box bearing fault and the motor shaft bearing fault. By theoretical analysis, simulation studies, laboratory analysis and actual applications, a combination of monitoring and diagnosis method is systematic studied. The main research content and results are as follows:
(1) The structure and mechanical characteristics of the train wheel set component is analyses in this part. The vibration characteristics, fault mechanism and fault spectrum characteristics is researched. The common fault frequency characteristics of the train wheel set component are summarized. Through the detail analysis of the external influences which is different from the common rotating machinery, the concept of frequency-varying factors is summarized and presented. By entirely think of the operation factors, the general fault mechanism model is established.
(2) By the definition of the fault characteristic coefficient K which is unrelated to the frequency-varying factors, the conventional spectral analysis method can be normalized to the characteristic domain signal, and the characteristics spectral analysis method is achieved. The principle and some key technologies (such as:synchronous sampling design, sampling parameters selected et al.) of characteristic spectral analysis method is analysed in detail. Through simulation analysis and experimental study on the train wheel set fault test system, the accuracy and usefulness of characteristics spectral analysis method is verified.
(3) In this part, the train likely-cycle vibration signal is analysised. By research the cyclostationary properties of the frequency-varying fault signal, the spectral correlation density function analysis method of train wheel set vibration signal's cycle statistics parmeter is selected. From the circulating the auto-correlation function of train wheel set vibration signals, the noise reduction analysis is proposed. The noise characteristic of the train wheel set vibration signal is researched through spectral correlation density function based on the auto-correlation function analysis of additive noise. And the noise reduction effect is verified by the simulated signals and experimental data analysis.
(4) The cyclic statistics method of is applied to extraction the fault characteristic of the frequency-varying fault. For calculating the cyclostationary properties as cycle frequency α, a cycle frequency a calculate algorithm based on the full frequency band sweep extraction method is proposed. By calculate the spectral correlation density function value where the cycle frequency a is get from the frequency band sweep method. The fault characteristic is extracted from the wheel components to achieve a precise diagnosis for such fault. In the last of this part, a higher order bispectrum analysis is applied to the train wheel set fault. Be combined with the characteristic domain signal processing method proposed in this paper. The characteristic domain's higher order bispectrum is put forward. And the characteristic domain bispectrum diagonal slice is used to extract fault characteristic. Through the practical application research, it shows that such analysis method has a certain practicality.
(5) The train wheel set frequency-varying fault signal's local mean decomposition method (denoted by LMD) is studied in this part. Full account of the multi-component AM-FM signal collect from train wheel set fault, the local mean decomposition method is applied to decompose this signal. The new local mean decomposition method based on the average processing with window sliding extraction technology is proposed and will be applied to extract a multi-component signal into several single-component signals. In order to achieve the no speed-tracking characteristic domain signal analysis techniques, the train wheel set instantaneous frequency of rotating frequency is extract from the several signal-component signals. By analyzing the principles and causes of the end effect in the local mean decomposition, a extension characteristic wave method based on the statistical characteristic of the original signal ending localized waveform is proposed. In order to eliminate the end effect of the decomposition algorithm, an intensity formula of the end effect's quantitative analysis is defined. All the method put forwarded above is verified in the real case application, and achieved satisfactory results.
(6) For the non-stationary and non-linear characteristics of the fault signal, a time-frequency analysis method is researched in order to extract the fault signal's time-frequency characteristic. A variables improved method of the kernel function is researched. And an amendments polynomial Wigner-Ville distribution for the train wheel real-time status monitoring is proposed. For the polynomial kernel function of the signal frequency distribution map is more complicated, a frequency map optimal path search algorithm based on the Viterbi algorithm is proposed. Through the experiment on the train wheel set fault test system and the actual train running data analysis, the methods researched above is verified, and obtain a satisfactory result.
The fault diagnosis analysis methods which are proposed in this paper are all undergoing by a rigorous theoretical analysis, simulation and real experiment has a strong practical application of engineering. This paper presents analytical methods is account of the frequency-varying factors, the precise diagnostic algorithm is put forward and verified. Such monitoring and diagnostic methods and techniques are not only can used to the train wheel set components, but also can promote the use of any possible speed fluctuations rotating machinery monitoring and diagnosis.
引文
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