低速行驶地铁轨道振动信号小波去噪参数研究
|
摘要
针对地铁低速行驶时的轨道振动信号,分析小波去噪参数的选取对去噪效果的影响。以北京某地铁站附近的隧道断面的轨道铅垂向振动加速度信号为例,对轨道振动信号进行小波分解、阈值去噪和重构。以信噪比作为去噪质量评价指标,采用Kruskal-Wallis非参数检验方法分析小波系、小波阶数、小波分解层数和阈值的选取对去噪效果的影响。基于信噪比最大化原则,确定地铁低速行驶时轨道振动信号的最优小波去噪参数。结果表明:显著性水平0.05下,小波系、小波阶数的选取对地铁轨道振动信号小波去噪效果没有显著影响,不同的小波分解层数、阈值对地铁轨道振动信号小波去噪效果的影响不全相同;以Symlets A小波系的4阶小波为去噪小波基,经过3层小波分解,基于无偏似然估计阈值和软阈值函数进行小波去噪后的信号获得最大的信噪比,去噪后的振动信号保留了原始信号的峰值特征,同时信号曲线的光滑性有了显著改善。
According to the track vibration acceleration signal of metro with the train traveling at low speed, the influence of selection of wavelet de-noising parameters on the de-noising effect is analyzed. With the track acceleration test data of a section of a metro line in Beijing as an example, wavelet decomposition, threshold de-noising and reconstruction of track vibration signal are carried out. The signal-to-noise ratio is used as the evaluation index of the de-noising quality.Kruskal-Wallis non-parametric test method is used to analyze the influences of wavelet series, wavelet order, wavelet decomposition layer number and threshold selections on the de-noising effect. With the maximum signal-to-noise ratio as the target, the best wavelet de-noising parameters of the metro track vibration signal is determined. The results show that the selection of wavelet series and wavelet order has no significant influences on the wavelet de-noising effect of the metro track vibration signal, and the influences of different wavelet decomposition layer number and the threshold on the wavelet denoising effect of the metro track vibration signal are different. The maximum signal-to-noise ratio is obtained based on sym4 wavelet base, Stein's unbiased risk threshold and soft threshold function. The signal after de-noising preserves the peak characteristic of the original signal, and the smoothness of the signal curve has been greatly improved.
According to the track vibration acceleration signal of metro with the train traveling at low speed, the influence of selection of wavelet de-noising parameters on the de-noising effect is analyzed. With the track acceleration test data of a section of a metro line in Beijing as an example, wavelet decomposition, threshold de-noising and reconstruction of track vibration signal are carried out. The signal-to-noise ratio is used as the evaluation index of the de-noising quality.Kruskal-Wallis non-parametric test method is used to analyze the influences of wavelet series, wavelet order, wavelet decomposition layer number and threshold selections on the de-noising effect. With the maximum signal-to-noise ratio as the target, the best wavelet de-noising parameters of the metro track vibration signal is determined. The results show that the selection of wavelet series and wavelet order has no significant influences on the wavelet de-noising effect of the metro track vibration signal, and the influences of different wavelet decomposition layer number and the threshold on the wavelet denoising effect of the metro track vibration signal are different. The maximum signal-to-noise ratio is obtained based on sym4 wavelet base, Stein's unbiased risk threshold and soft threshold function. The signal after de-noising preserves the peak characteristic of the original signal, and the smoothness of the signal curve has been greatly improved.
引文
[1]刘维宁,马蒙.地铁列车振动环境影响的预测、评估与控制[M].北京:科学出版社,2014.
[2]刘维宁,马蒙,刘卫丰,等.我国城市轨道交通环境振动影响的研究现状[J].中国科学中国科学:技术科学,2016,46(6):547-559.
[3]胡燚斌,王冠,刑俊,等.小波变换-回归分析模型在轨道不平顺检测中的应用[J].铁道建筑,2016(5):148-151.
[4]徐磊,陈宪麦.基于小波和Wigner-Hough变换的轨道特征不平顺联合分析[J].铁道学报,2014,36(5):88-95.
[5]何越磊,陈施宇,李再帏.基于小波变换的轨道不平顺数值模拟方法[J].城市轨道交通研究,2014,17(10):59-62.
[6]宁迎智,周小林,徐庆元,等.基于小波的轨道不平顺和车体振动响应分析[J].铁道科学与工程学报报,2013,10(3):28-32.
[7]董伟,黄文,刑宗义,等.基于小波包能量熵的钢轨波磨检测方法[J].铁道标准设计,2018,62(1):52-58.
[8]翟国锐,贺延芳,魏秀琨.城市轨道交通钢轨波浪形磨耗检测仿真研究[J].北京交通大学学报,2017,41(4):77-84+90.
[9]丁建明,林建辉,赵洁,等.车辆悬挂弹簧故障检测的能量传递特性比较法[J].交通运输工程学报,2013,13(4):51-55+62.
[10]韩晋,杨岳,陈峰,吴湘华,刘希玲.基于小波变换的轨道检测数据滤波方法[J].铁道科学与工程学报,2013,10(5):116-122.
[11]李智敏,苟先太,秦娜,等.高速列车振动监测信号的频率特征[J].仪表技术与传感器,2015(5):99-103.
[12]赵龙,卢建康,刘竹均.小波变换在高速铁路轨道精调中的应用[J].铁道工程学报,2017,34(4):30-35.
[13]余腾,胡伍生,赵升峰,等.基于运营期地铁轨行区监测数据的去噪小波基函数选取[J].铁道标准设计,2017,61(8):116-120.
[14]王宗炼,任会兰,宁建国.基于小波变换降噪的声发射源定位方法[J].振动与冲击,2018,37(4):226-232+248.
[15]陶珂,朱建军.小波去噪质量评价方法的对比研究[J].大地测量与地球动力学,2012,32(2):128-133.
[16]陈文会,丁晓鸿,陈江宁,等.超声波测距信号小波阈值去噪参数的选定方法[J].传感技术学报,2017,30(3):407-411.
[17]刘海江,李敏,黄伟,等.基于小波变换的整车驾驶性评价信号去噪[J].噪声与振动控制,2018,38(1):103-108.
[18]田丰,孙剑,邵山.小波阈值去噪在传感器性能试验数据处理中的应用[J].传感器与微系统,2014,33(6):143-146.
[19]夏云云,文尚胜,方方.基于Kolmogorov-Smirnov检验的LED可靠性评估[J].光子学报,2016,45(9):26-31.
[20]董寒青.非参数统计与SPSS/R/SAS软件应用[M].北京:中国统计出版社,2018.
[21]吴彪,杨忠振,许洪国,等.高速公路施工区车速差异显著性测定[J].福州大学学报(自然科学版),2016,44(4):563-569.
[22]熊婧,石岩,吴建敏,等.基于非参数检验分析化学药品引湿性与水溶解性的关系[J].中国药学杂志,2016,51(20):1786-1789.
[23]张志俊,王盼盼.基于Kruskal-Wallis检验的公路货运车辆实载率差异分析[J].交通运输系统工程与信息,2014,14(5):216-220+241.
[2]刘维宁,马蒙,刘卫丰,等.我国城市轨道交通环境振动影响的研究现状[J].中国科学中国科学:技术科学,2016,46(6):547-559.
[3]胡燚斌,王冠,刑俊,等.小波变换-回归分析模型在轨道不平顺检测中的应用[J].铁道建筑,2016(5):148-151.
[4]徐磊,陈宪麦.基于小波和Wigner-Hough变换的轨道特征不平顺联合分析[J].铁道学报,2014,36(5):88-95.
[5]何越磊,陈施宇,李再帏.基于小波变换的轨道不平顺数值模拟方法[J].城市轨道交通研究,2014,17(10):59-62.
[6]宁迎智,周小林,徐庆元,等.基于小波的轨道不平顺和车体振动响应分析[J].铁道科学与工程学报报,2013,10(3):28-32.
[7]董伟,黄文,刑宗义,等.基于小波包能量熵的钢轨波磨检测方法[J].铁道标准设计,2018,62(1):52-58.
[8]翟国锐,贺延芳,魏秀琨.城市轨道交通钢轨波浪形磨耗检测仿真研究[J].北京交通大学学报,2017,41(4):77-84+90.
[9]丁建明,林建辉,赵洁,等.车辆悬挂弹簧故障检测的能量传递特性比较法[J].交通运输工程学报,2013,13(4):51-55+62.
[10]韩晋,杨岳,陈峰,吴湘华,刘希玲.基于小波变换的轨道检测数据滤波方法[J].铁道科学与工程学报,2013,10(5):116-122.
[11]李智敏,苟先太,秦娜,等.高速列车振动监测信号的频率特征[J].仪表技术与传感器,2015(5):99-103.
[12]赵龙,卢建康,刘竹均.小波变换在高速铁路轨道精调中的应用[J].铁道工程学报,2017,34(4):30-35.
[13]余腾,胡伍生,赵升峰,等.基于运营期地铁轨行区监测数据的去噪小波基函数选取[J].铁道标准设计,2017,61(8):116-120.
[14]王宗炼,任会兰,宁建国.基于小波变换降噪的声发射源定位方法[J].振动与冲击,2018,37(4):226-232+248.
[15]陶珂,朱建军.小波去噪质量评价方法的对比研究[J].大地测量与地球动力学,2012,32(2):128-133.
[16]陈文会,丁晓鸿,陈江宁,等.超声波测距信号小波阈值去噪参数的选定方法[J].传感技术学报,2017,30(3):407-411.
[17]刘海江,李敏,黄伟,等.基于小波变换的整车驾驶性评价信号去噪[J].噪声与振动控制,2018,38(1):103-108.
[18]田丰,孙剑,邵山.小波阈值去噪在传感器性能试验数据处理中的应用[J].传感器与微系统,2014,33(6):143-146.
[19]夏云云,文尚胜,方方.基于Kolmogorov-Smirnov检验的LED可靠性评估[J].光子学报,2016,45(9):26-31.
[20]董寒青.非参数统计与SPSS/R/SAS软件应用[M].北京:中国统计出版社,2018.
[21]吴彪,杨忠振,许洪国,等.高速公路施工区车速差异显著性测定[J].福州大学学报(自然科学版),2016,44(4):563-569.
[22]熊婧,石岩,吴建敏,等.基于非参数检验分析化学药品引湿性与水溶解性的关系[J].中国药学杂志,2016,51(20):1786-1789.
[23]张志俊,王盼盼.基于Kruskal-Wallis检验的公路货运车辆实载率差异分析[J].交通运输系统工程与信息,2014,14(5):216-220+241.