非平稳运行时列车轮对轴承道旁声学故障诊断方法研究
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摘要
本文以我国现役列车轮对轴承NJ(P)3226X1为研究对象,以道旁声学监测与故障诊断策略为研究内容,重点对道旁信号畸变矫正和基于矫正信号的故障诊断策略进行了深入的探讨。
第一,通过实验获取了轴承声源直线运动时产生的具有畸变特性的道旁声学信号,分析了其畸变特性,初步探讨了流场畸变的成因和特性。
第二,以莫尔斯声学理论和插值采样方法为基础,提出了时域插值多普勒矫正方法。该方法既考虑了信号的频域结构矫正,也考虑了信号的时域幅值还原,使得信号得到了最大程度的矫正恢复。
第三,提出了道旁信号自适应多普勒矫正策略。首先是对Doppler-let原有公式进行了修正,重新定义了约束参数,提出了“时幅映射插值构造法”,实现了离散Doppler-let的构造。其次是以Doppler-let和匹配追踪信号分解方法为基础,通过在参数域优化搜索的方法实现了道旁模型参数的自动识别。有效解决了道旁模型参数自动获取问题,走出了“几何测量代替实际参数”的误区,并且考虑了声源作非匀速运行的工况,使道旁多普勒畸变信号得以更高精度的矫正,尤其适用于地铁列车非匀速运行的工况。
第四,探讨了基于窄带信号瞬时频率和重采样技术在列车轮对轴承道旁畸变信号矫正中的应用。该方法首先通过带通滤波从道旁信号中提取窄带信号,然后通过Hilbert变换计算窄带信号的瞬时频率,最后以得到的瞬时频率为依据对畸变信号进行重采样矫正,通过对比分析总结了该方法的优缺点。
第五,在信号得到矫正之后,探讨了基于矫正信号的故障诊断策略。作了两方面的探讨,第一是将EEMD方法用来提取蕴含故障信息的本征模态信号,结合使用共振解调方法进行了特征提取和故障辨识；第二是将瞬态成分分析用于矫正信号的故障辨识,通过参数化周期单边Laplace小波与道旁矫正信号的匹配分析提取了故障特征,实现了故障辨识。
The train bearing with the type of NJ(P)3226X1which is the type employed in our country, is selected to be the research object in our study. The main topic in this paper is the wayside acoustic health monitoring technique, involving two main issues: the distortion removal for the recorded signal and the fault diagnosis strategy for the train bearing.
The wayside acoustic signal generated by the train bearing is obtained. The reason and characteristics of the "flow field distortion" is studied.
A time domain interpolation resampling method is proposed based on the Morse acoustic theory and interpolation method. Through this method, the frequency-domain distortion as well as the time domain distortion can be both corrected, such that the distored signal obtains a maximum recovery.
A data-driven Doppler effect elimination strategy is investigated in this paper. Firstly, the error of the existed formula has been pointed out. Then the construction parameters have been redefined and a "time-amplitude mapping interpolation method" is introduced to construct a Doppler-let. Secondly, based on Doppler-lets and matching pursuit method, the wayside-model parameters are identified by optimizing in the parameters set. Non-uniform operating condition is also considered in this method, so that the Doppler-shifted signal can be corrected much more accurately, especially for subway trains running at non-uniform speed.
The instantaneous frequency estimation method and the signal resampling method are employed in this paper to correct the Doppler-shifted signal. The narrowband signal is obtained by band-pass filtering, and then the instantaneous frequency of the narrowband signal is calculated by Hilbert transform, and finally the Doppler-shifted signal is resampled according to the obtained instantaneous frequency curve. Both the advantages and disadvantages of this method are analyzed and summarized by comparing with another method.
Two fault diagnosis strategies based on the corrected signal are introduced in this paper. One strategy is as follows:firstly the EEMD method is employed to extract the Intrinsic Mode Function (IMF) containg the fault information, then, the resonance demodulation method is employed to extract the feature. The other strategy involves the transient model analysis, the feature is extraced by matching the parametric periodic Laplace wavelets with the corrected signal.
第一,通过实验获取了轴承声源直线运动时产生的具有畸变特性的道旁声学信号,分析了其畸变特性,初步探讨了流场畸变的成因和特性。
第二,以莫尔斯声学理论和插值采样方法为基础,提出了时域插值多普勒矫正方法。该方法既考虑了信号的频域结构矫正,也考虑了信号的时域幅值还原,使得信号得到了最大程度的矫正恢复。
第三,提出了道旁信号自适应多普勒矫正策略。首先是对Doppler-let原有公式进行了修正,重新定义了约束参数,提出了“时幅映射插值构造法”,实现了离散Doppler-let的构造。其次是以Doppler-let和匹配追踪信号分解方法为基础,通过在参数域优化搜索的方法实现了道旁模型参数的自动识别。有效解决了道旁模型参数自动获取问题,走出了“几何测量代替实际参数”的误区,并且考虑了声源作非匀速运行的工况,使道旁多普勒畸变信号得以更高精度的矫正,尤其适用于地铁列车非匀速运行的工况。
第四,探讨了基于窄带信号瞬时频率和重采样技术在列车轮对轴承道旁畸变信号矫正中的应用。该方法首先通过带通滤波从道旁信号中提取窄带信号,然后通过Hilbert变换计算窄带信号的瞬时频率,最后以得到的瞬时频率为依据对畸变信号进行重采样矫正,通过对比分析总结了该方法的优缺点。
第五,在信号得到矫正之后,探讨了基于矫正信号的故障诊断策略。作了两方面的探讨,第一是将EEMD方法用来提取蕴含故障信息的本征模态信号,结合使用共振解调方法进行了特征提取和故障辨识；第二是将瞬态成分分析用于矫正信号的故障辨识,通过参数化周期单边Laplace小波与道旁矫正信号的匹配分析提取了故障特征,实现了故障辨识。
The train bearing with the type of NJ(P)3226X1which is the type employed in our country, is selected to be the research object in our study. The main topic in this paper is the wayside acoustic health monitoring technique, involving two main issues: the distortion removal for the recorded signal and the fault diagnosis strategy for the train bearing.
The wayside acoustic signal generated by the train bearing is obtained. The reason and characteristics of the "flow field distortion" is studied.
A time domain interpolation resampling method is proposed based on the Morse acoustic theory and interpolation method. Through this method, the frequency-domain distortion as well as the time domain distortion can be both corrected, such that the distored signal obtains a maximum recovery.
A data-driven Doppler effect elimination strategy is investigated in this paper. Firstly, the error of the existed formula has been pointed out. Then the construction parameters have been redefined and a "time-amplitude mapping interpolation method" is introduced to construct a Doppler-let. Secondly, based on Doppler-lets and matching pursuit method, the wayside-model parameters are identified by optimizing in the parameters set. Non-uniform operating condition is also considered in this method, so that the Doppler-shifted signal can be corrected much more accurately, especially for subway trains running at non-uniform speed.
The instantaneous frequency estimation method and the signal resampling method are employed in this paper to correct the Doppler-shifted signal. The narrowband signal is obtained by band-pass filtering, and then the instantaneous frequency of the narrowband signal is calculated by Hilbert transform, and finally the Doppler-shifted signal is resampled according to the obtained instantaneous frequency curve. Both the advantages and disadvantages of this method are analyzed and summarized by comparing with another method.
Two fault diagnosis strategies based on the corrected signal are introduced in this paper. One strategy is as follows:firstly the EEMD method is employed to extract the Intrinsic Mode Function (IMF) containg the fault information, then, the resonance demodulation method is employed to extract the feature. The other strategy involves the transient model analysis, the feature is extraced by matching the parametric periodic Laplace wavelets with the corrected signal.
引文
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