列车轴承故障道旁声学诊断关键技术研究
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摘要
铁路运输在我国国民经济和现代化建设中发挥了十分重要的作用,而作为铁路运输主要运载工具的列车,其运行过程中的安全问题则直接影响到人民生命财产安全以及国民经济的发展。列车轮对轴承故障是导致列车发生故障甚至脱轨事故的最主要的原因,长期以来受到国内外各界的广泛关注。因此对列车轴承进行状态监测及故障诊断是非常必要的,对列车的安全运行具有重要意义。对比传统列车车载监测系统,列车轴承故障道旁声学监测与诊断系统具备诸多优点,但也同时存在“陡畸变、多声源、强噪声”这些声信号处理上的技术难点,阻碍了其自主化国产化的进程。
本文围绕列车轴承故障道旁声学诊断存在的关键技术难题进行了研究与探讨,提出了多普勒畸变校正方法、多声源分离及强噪声滤除方法以及列车轴承道旁声信号故障特征提取方法,并对这些方法进行了理论推导与实验验证,以期为最终实现全自主化国产化列车轴承故障道旁声学监测与诊断系统奠定一定理论基础。主要研究内容如下：
首先分析了滚动轴承的结构、故障特征频率以及主要的失效形式,阐述了振动产生的机理,同时也分析了轴承内噪声信号产生的原理以及与振动之间的联系。并针对我国现役客运列车轴承NJ(P)3226X1,设计了一个声信号采集实验平台,通过线切割对列车轴承设置了人工故障,以获取不同状况下的列车轴承声信号,为后续解决列车轴承故障道旁声学诊断的关键技术难题提供研究对象与理论依据。
详细阐述了多普勒畸变的基本概念与原理,并从运动声学的角度建立了道旁声信号的多普勒畸变模型,推导了多普勒畸变的数学公式。研究分析了现有的多普勒畸变校正方法的一些缺陷与局限性,提出了一种基于匹配追踪Dopplerlet变换与重采样的多普勒畸变校正方法。该方法可以在不知道初始运动参数的情况下实现单点声源多普勒畸变校正,或是对多个点声源进行逐一校正。通过抗噪性分析可以发现此种方法对随机噪声具有很强的鲁棒性。最后针对轴承内、外圈故障的实验声信号进行分析和处理,实验结果成功地对该方法的有效性与可行性进行了验证。
深入讨论了信号分离的研究现状,阐述了广义s变换的基本概念以及实现算法,讨论了基于广义S变换及时频域滤波的信号重构方法细节,同时也给出了相应的仿真案例。由于从单通道信号中实现高速运动信号有效分离的理论方法在国内外研究中都比较空缺,本文结合匹配追踪Dopplerlet变换其独有的多普勒信号匹配特性,提出了一种基于匹配追踪Dopplerlet变换、广义S变换及时频滤波的信号重构方法,来进行高速运动信号的有效分离,进而实现列车轴承道旁声信号多声源分离和强噪声滤除的目的。
通过分析旋转机械的状态信号特有的周期性脉冲频率特征以及旁瓣现象,结合小波多尺度分析的方法,提出了一种用于列车轴承声信号故障特征提取的小波尺度方差斜率特征提取方法。通过对正常状态、外圈故障、内圈故障以及滚子故障的列车轴承声信号共200段信号进行了分析和验证,实验结果表明将小波尺度方差斜率特征作为轴承故障特征应用在列车轴承故障轨边声学诊断中具有很好的聚类性和稳定性。
Railway transportation plays a very important role in the national economy and the modernization of our country, but the train as a major railway transportation vehicle, the safety problems in the process of its operation directly affect people's life and property safety and the development of national economy. Train wheel bearing failure is the main cause of the train derailment accident or malfunction, which has attracted extensive attention from home and abroad. Thus the train bearing condition monitoring and fault diagnosis is very necessary and of great significance to the safe operation of the train. Compared with the traditional train vehicle monitoring systems, although the wayside acoustic defective bearing detector system has many advantages, the technological difficulties of acoustic signal processing, such as steep distortion, multiple sound sources, and strong noises still hindering the process of its autonomy and localization.
The major research of this paper focuses on the key technical problems of the wayside acoustic fault diagnosis of train bearings, and the Doppler distortion remove method, multiple sound sources separation and strong noise filtering methods and the train bearing acoustic signal fault feature extraction methods are proposed in this paper. And these methods are theoretically elicited and experimentally verified, in order to lay a certain theoretical foundation of the final realization of the homemade wayside acoustic defective bearing detector system. The main research contents are as follows:
Firstly the structure, fault characteristic frequency and the main failure forms of rolling bearing were discussed, the mechanism of vibration was elaborated. The analysis of the principle of bearing noise signal and the relationship between noise and vibration was given. An acoustic signal acquisition experimental platform was designed for the active duty passenger train bearing NJ (P)3226X1. The artificial cracks had been set by the wire-electrode cutting machine on the train bearings to obtain the train bearing acoustic signals under different fault conditions, which provide the research object and theoretical basis for resolving the key technical problems of the wayside acoustic fault diagnosis of train bearings.
The basic concepts and principles of Doppler distortion were discussed in detail, and the Doppler distortion model of wayside acoustic signal was established form the view of moving acoustics, then a mathematical formula of Doppler distortion was derived. After analyzing the flaws and limitations of the existing Doppler distortion correction methods, a Doppler distortion correction method based on Matching Pursuit Dopplerlet transform(MPDT) and resample was proposed. This method can implement both the Doppler distortion correction of single acoustic source and the Doppler distortion correction of multiple acoustic sources one by one, without knowing the initial motion parameters. Meanwhile, this method was robust to random noise by anti-noise analysis. Finally, based on the analysis and processing of train bearing outer race fault and inner race fault acoustic signals, the experimental results demonstrated the effectiveness and feasibility of this method.
A detailed discussion of the current research of signal separation was presented. Then the basic concepts and algorithm of generalized S transform(GST) were given, the details of signal reconstruction method based on the GST and time-frequency filtering(TFF) were discussed, and the corresponding simulation case was also given. As the theoretical method of separating high-speed motion acoustic signal from a single channel signal is extremely vacant in the research at home and abroad, based on the Doppler signal matching features of MPDT, a signal reconstruction method based on MPDT, GST and TFF was proposed to separate high-speed motion acoustic signal, thus achieving the purposes of multiple sound sources separation and strong noise filtering of wayside train bearing acoustic signals.
By analyzing unique periodic pulse frequency and side-lobe phenomenon of the rotating machinery status signals, a method of wavelet scale variance slope feature extraction for wayside train bearing acoustic signals feature extraction was proposed, based on the method of wavelet multiscale analysis. Then this method was applied in the analysis of a total of200segments train bearing acoustic signals, which contains normal condition signals, outer race fault signals, inner race fault signals and roller fault signals. Finally, the experimental results showed that, as a train bearing fault feature, the wavelet scale variance slope feature had good properties of clustering and stability in the application of wayside acoustic fault diagnosis of train bearings.
本文围绕列车轴承故障道旁声学诊断存在的关键技术难题进行了研究与探讨,提出了多普勒畸变校正方法、多声源分离及强噪声滤除方法以及列车轴承道旁声信号故障特征提取方法,并对这些方法进行了理论推导与实验验证,以期为最终实现全自主化国产化列车轴承故障道旁声学监测与诊断系统奠定一定理论基础。主要研究内容如下：
首先分析了滚动轴承的结构、故障特征频率以及主要的失效形式,阐述了振动产生的机理,同时也分析了轴承内噪声信号产生的原理以及与振动之间的联系。并针对我国现役客运列车轴承NJ(P)3226X1,设计了一个声信号采集实验平台,通过线切割对列车轴承设置了人工故障,以获取不同状况下的列车轴承声信号,为后续解决列车轴承故障道旁声学诊断的关键技术难题提供研究对象与理论依据。
详细阐述了多普勒畸变的基本概念与原理,并从运动声学的角度建立了道旁声信号的多普勒畸变模型,推导了多普勒畸变的数学公式。研究分析了现有的多普勒畸变校正方法的一些缺陷与局限性,提出了一种基于匹配追踪Dopplerlet变换与重采样的多普勒畸变校正方法。该方法可以在不知道初始运动参数的情况下实现单点声源多普勒畸变校正,或是对多个点声源进行逐一校正。通过抗噪性分析可以发现此种方法对随机噪声具有很强的鲁棒性。最后针对轴承内、外圈故障的实验声信号进行分析和处理,实验结果成功地对该方法的有效性与可行性进行了验证。
深入讨论了信号分离的研究现状,阐述了广义s变换的基本概念以及实现算法,讨论了基于广义S变换及时频域滤波的信号重构方法细节,同时也给出了相应的仿真案例。由于从单通道信号中实现高速运动信号有效分离的理论方法在国内外研究中都比较空缺,本文结合匹配追踪Dopplerlet变换其独有的多普勒信号匹配特性,提出了一种基于匹配追踪Dopplerlet变换、广义S变换及时频滤波的信号重构方法,来进行高速运动信号的有效分离,进而实现列车轴承道旁声信号多声源分离和强噪声滤除的目的。
通过分析旋转机械的状态信号特有的周期性脉冲频率特征以及旁瓣现象,结合小波多尺度分析的方法,提出了一种用于列车轴承声信号故障特征提取的小波尺度方差斜率特征提取方法。通过对正常状态、外圈故障、内圈故障以及滚子故障的列车轴承声信号共200段信号进行了分析和验证,实验结果表明将小波尺度方差斜率特征作为轴承故障特征应用在列车轴承故障轨边声学诊断中具有很好的聚类性和稳定性。
Railway transportation plays a very important role in the national economy and the modernization of our country, but the train as a major railway transportation vehicle, the safety problems in the process of its operation directly affect people's life and property safety and the development of national economy. Train wheel bearing failure is the main cause of the train derailment accident or malfunction, which has attracted extensive attention from home and abroad. Thus the train bearing condition monitoring and fault diagnosis is very necessary and of great significance to the safe operation of the train. Compared with the traditional train vehicle monitoring systems, although the wayside acoustic defective bearing detector system has many advantages, the technological difficulties of acoustic signal processing, such as steep distortion, multiple sound sources, and strong noises still hindering the process of its autonomy and localization.
The major research of this paper focuses on the key technical problems of the wayside acoustic fault diagnosis of train bearings, and the Doppler distortion remove method, multiple sound sources separation and strong noise filtering methods and the train bearing acoustic signal fault feature extraction methods are proposed in this paper. And these methods are theoretically elicited and experimentally verified, in order to lay a certain theoretical foundation of the final realization of the homemade wayside acoustic defective bearing detector system. The main research contents are as follows:
Firstly the structure, fault characteristic frequency and the main failure forms of rolling bearing were discussed, the mechanism of vibration was elaborated. The analysis of the principle of bearing noise signal and the relationship between noise and vibration was given. An acoustic signal acquisition experimental platform was designed for the active duty passenger train bearing NJ (P)3226X1. The artificial cracks had been set by the wire-electrode cutting machine on the train bearings to obtain the train bearing acoustic signals under different fault conditions, which provide the research object and theoretical basis for resolving the key technical problems of the wayside acoustic fault diagnosis of train bearings.
The basic concepts and principles of Doppler distortion were discussed in detail, and the Doppler distortion model of wayside acoustic signal was established form the view of moving acoustics, then a mathematical formula of Doppler distortion was derived. After analyzing the flaws and limitations of the existing Doppler distortion correction methods, a Doppler distortion correction method based on Matching Pursuit Dopplerlet transform(MPDT) and resample was proposed. This method can implement both the Doppler distortion correction of single acoustic source and the Doppler distortion correction of multiple acoustic sources one by one, without knowing the initial motion parameters. Meanwhile, this method was robust to random noise by anti-noise analysis. Finally, based on the analysis and processing of train bearing outer race fault and inner race fault acoustic signals, the experimental results demonstrated the effectiveness and feasibility of this method.
A detailed discussion of the current research of signal separation was presented. Then the basic concepts and algorithm of generalized S transform(GST) were given, the details of signal reconstruction method based on the GST and time-frequency filtering(TFF) were discussed, and the corresponding simulation case was also given. As the theoretical method of separating high-speed motion acoustic signal from a single channel signal is extremely vacant in the research at home and abroad, based on the Doppler signal matching features of MPDT, a signal reconstruction method based on MPDT, GST and TFF was proposed to separate high-speed motion acoustic signal, thus achieving the purposes of multiple sound sources separation and strong noise filtering of wayside train bearing acoustic signals.
By analyzing unique periodic pulse frequency and side-lobe phenomenon of the rotating machinery status signals, a method of wavelet scale variance slope feature extraction for wayside train bearing acoustic signals feature extraction was proposed, based on the method of wavelet multiscale analysis. Then this method was applied in the analysis of a total of200segments train bearing acoustic signals, which contains normal condition signals, outer race fault signals, inner race fault signals and roller fault signals. Finally, the experimental results showed that, as a train bearing fault feature, the wavelet scale variance slope feature had good properties of clustering and stability in the application of wayside acoustic fault diagnosis of train bearings.
引文
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