基于信号理论的桥梁健康监测降噪处理和损伤识别研究
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摘要
桥梁结构通常位于复杂的外界环境中,在各种因素的影响下,桥梁健康监测系统总是受到噪声的干扰,结构的大量状态信息淹没在噪声之中,导致损伤识别率偏低,使得无法直接通过采样信号准确识别结构损伤。因此,在信号分析过程中,必须首先对采样信号进行相应的降噪处理,最大限度地消除噪声的影响并突出结构信息,才能更为有效地完成测试数据分析,达到结构损伤识别的最佳效果。
本文围绕桥梁健康监测采样信号降噪处理的三个关键问题：有用信号与噪声的特征差异、降噪处理算法的构建以及在结构损伤识别中的应用验证开展研究工作。将相关检测、傅里叶变换、滤波器、小波变换、希尔伯特-黄变换(HHT)和自然激励技术(NExT)等信号处理方法引入采样信号降噪处理和结构损伤识别过程,根据有用信号与噪声的特征差异,提出不同类型噪声的降噪处理算法,并进行结构的损伤预警和损伤定位研究,构建了相对完整的基于信号降噪处理的桥梁损伤识别体系。本文主要研究工作包括：
1.通过查阅大量国内外文献,对桥梁健康监测及相关领域的采样信号降噪处理研究思路和方法进行了回顾和总结,并提出桥梁健康监测降噪研究工作的三个内容,即有用信号与噪声的特征差异、降噪处理算法的构建以及在结构损伤识别中的应用验证。
2.总结和分析得到桥梁健康监测系统有用信号与噪声的特征差异,并提出相应的处理建议。将噪声概念与桥梁健康监测中的测量误差相对应,提出降噪处理顺序,并对待处理噪声特征特别是与有用信号的差异进行较为详细的分析。
3.对桥梁健康监测采样信号中存在的非白噪声白化处理进行了研究。根据不同类型的非白噪声特征,基于HHT、小波变换和切比雪夫滤波器等现代信号变换理论,提出了基于EMD参数识别的工频滤波白化算法和1/f噪声的分频小波阈值白化算法,并通过仿真试验验证了算法的白化处理适用性和有效性。
4.对桥梁健康监测采样信号中存在的白噪声降噪处理进行了研究,针对桥梁健康监测采样信号特征以及传统算法存在的不足,综合EMD、小波变换和相关检测三种方法的优点,提出一种白噪声降噪新算法,称为EMD小波相关降噪算法。仿真试验结果表明,该算法受各种参数的影响程度较小,非常适合用于频率较低、信噪比较小和信号细节成份丰富的桥梁健康监测采样信号降噪处理。
5.对基于信号联合降噪处理的结构损伤识别进行了研究,重点验证了在不同工况条件下信号降噪对结构损伤识别效果和损伤识别率提高的有效性。首先将非白噪声和白噪声处理方法相结合,构建采样信号的联合降噪算法,采用该算法进行桥梁健康监测采样信号混合噪声的降噪处理,有限元仿真试验结果表明,联合降噪算法适合用于桥梁健康监测采样信号降噪处理。采用目前较为成熟的基于小波包能量的损伤预警算法和本文提出的基于NExT的小波包能量损伤定位算法,进行联合降噪前后损伤识别效果和损伤识别率对比研究,桥梁结构有限元仿真试验验证了本文提出的联合降噪算法在桥梁结构损伤识别应用中的适用性和有效性,经降噪处理后的结构损伤预警和损伤定位能力均得到较大程度的提高。
Bridge health monitoring system is interfered by noise inevitably because of the complicated environment. A large number of structure behavior information is submerged in noise, which results in relatively low damage identification rate if sampling signals are used directly. Consequently, noise reduction should be carried out firstly in signal analysis, which can eliminate noise influence and highlight structure information furthest, and then the best effect of structure damage identification will be achieved.
Three keys of noise reduction in bridge health monitoring which are the feature difference of useful signal and noise, the establishment of noise reduction algorithm and the application of structure damage identification are researched in the thesis. Several signal process methods such as correlation detection, Fourier transform, filter, wavelet transform, Hilbert-Huang transform (HHT) and natural excitation technique (NExT) are used in sampling signal noise reduction and structure damage identification. According to the feature difference of useful signal and noise, reduction algorithms of different types of noise are proposed, structure damage alarming and localization are studied, and relatively integrated bridge damage identification system is established. The main researches include as below:
1. Review and summary of research ideas and methods are carried out in bridge health monitoring and related area by reading massive domestic and overseas literatures. Three contents which are the feature difference of useful signal and noise, the establishment of noise reduction method and the application of structure damage identification are proposed in the research on bridge health monitoring noise reduction.
2. The feature difference of useful signal and noise in bridge health monitoring system is analyzed and summarized, and the corresponding suggestions are presented. With the concept comparison of noise and measure error in bridge health monitoring, the procedure of noise reduction is proposed and analysis of noise features especially the difference to useful signal is carried out in detail.
3. Research on whitening process of non-white noise in bridge health monitoring is proceeded. On the basis of various types of non-white noise features and modern signal theories such as HHT, wavelet transform and Chebyshev filter, industrial noise whitening process based on EMD parameter detection and1/f noise wavelet threshold whitening method based on frequency range are proposed, whose applicability and effectiveness are verified by numerical simulation experiments.
4. Research on white noise reduction in bridge health monitoring is carried out. In allusion to the features of bridge health monitoring sampling signal and shortages of traditional algorithms, a new algorithm named EMD wavelet correlation noise reduction algorithm is proposed based on the advantages of EMD, wavelet transform and correlation detection. Simulation experiments demonstrate that the proposed algorithm has weak influence by each parameter, and it is very suitable for noise reduction of sampling signal with low frequency, weak signal to noise ratio and abundant details.
5. The effect and rate improvement of structure damage identification on different conditions is proved by the research on structure damage identification using integrated noise reduction algorithm, which is established by combination of non-white noise and white noise reduction method. Noise reduction in bridge health monitoring was carried out by the proposed algorithm and proved by the finite element simulation experiment. Effect and rate comparison of damage identification before and after integrated noise reduction is proceeded using traditional damage alarming algorithm based on wavelet packet energy and proposed damage localization algorithm based on NExT wavelet packet energy, the results of finite element simulation experiment confirm the applicability and effectiveness of integrated noise reduction algorithm in damage identification, and the effect of structure damage alarming and localization using de-noised signals increases significantly.
本文围绕桥梁健康监测采样信号降噪处理的三个关键问题：有用信号与噪声的特征差异、降噪处理算法的构建以及在结构损伤识别中的应用验证开展研究工作。将相关检测、傅里叶变换、滤波器、小波变换、希尔伯特-黄变换(HHT)和自然激励技术(NExT)等信号处理方法引入采样信号降噪处理和结构损伤识别过程,根据有用信号与噪声的特征差异,提出不同类型噪声的降噪处理算法,并进行结构的损伤预警和损伤定位研究,构建了相对完整的基于信号降噪处理的桥梁损伤识别体系。本文主要研究工作包括：
1.通过查阅大量国内外文献,对桥梁健康监测及相关领域的采样信号降噪处理研究思路和方法进行了回顾和总结,并提出桥梁健康监测降噪研究工作的三个内容,即有用信号与噪声的特征差异、降噪处理算法的构建以及在结构损伤识别中的应用验证。
2.总结和分析得到桥梁健康监测系统有用信号与噪声的特征差异,并提出相应的处理建议。将噪声概念与桥梁健康监测中的测量误差相对应,提出降噪处理顺序,并对待处理噪声特征特别是与有用信号的差异进行较为详细的分析。
3.对桥梁健康监测采样信号中存在的非白噪声白化处理进行了研究。根据不同类型的非白噪声特征,基于HHT、小波变换和切比雪夫滤波器等现代信号变换理论,提出了基于EMD参数识别的工频滤波白化算法和1/f噪声的分频小波阈值白化算法,并通过仿真试验验证了算法的白化处理适用性和有效性。
4.对桥梁健康监测采样信号中存在的白噪声降噪处理进行了研究,针对桥梁健康监测采样信号特征以及传统算法存在的不足,综合EMD、小波变换和相关检测三种方法的优点,提出一种白噪声降噪新算法,称为EMD小波相关降噪算法。仿真试验结果表明,该算法受各种参数的影响程度较小,非常适合用于频率较低、信噪比较小和信号细节成份丰富的桥梁健康监测采样信号降噪处理。
5.对基于信号联合降噪处理的结构损伤识别进行了研究,重点验证了在不同工况条件下信号降噪对结构损伤识别效果和损伤识别率提高的有效性。首先将非白噪声和白噪声处理方法相结合,构建采样信号的联合降噪算法,采用该算法进行桥梁健康监测采样信号混合噪声的降噪处理,有限元仿真试验结果表明,联合降噪算法适合用于桥梁健康监测采样信号降噪处理。采用目前较为成熟的基于小波包能量的损伤预警算法和本文提出的基于NExT的小波包能量损伤定位算法,进行联合降噪前后损伤识别效果和损伤识别率对比研究,桥梁结构有限元仿真试验验证了本文提出的联合降噪算法在桥梁结构损伤识别应用中的适用性和有效性,经降噪处理后的结构损伤预警和损伤定位能力均得到较大程度的提高。
Bridge health monitoring system is interfered by noise inevitably because of the complicated environment. A large number of structure behavior information is submerged in noise, which results in relatively low damage identification rate if sampling signals are used directly. Consequently, noise reduction should be carried out firstly in signal analysis, which can eliminate noise influence and highlight structure information furthest, and then the best effect of structure damage identification will be achieved.
Three keys of noise reduction in bridge health monitoring which are the feature difference of useful signal and noise, the establishment of noise reduction algorithm and the application of structure damage identification are researched in the thesis. Several signal process methods such as correlation detection, Fourier transform, filter, wavelet transform, Hilbert-Huang transform (HHT) and natural excitation technique (NExT) are used in sampling signal noise reduction and structure damage identification. According to the feature difference of useful signal and noise, reduction algorithms of different types of noise are proposed, structure damage alarming and localization are studied, and relatively integrated bridge damage identification system is established. The main researches include as below:
1. Review and summary of research ideas and methods are carried out in bridge health monitoring and related area by reading massive domestic and overseas literatures. Three contents which are the feature difference of useful signal and noise, the establishment of noise reduction method and the application of structure damage identification are proposed in the research on bridge health monitoring noise reduction.
2. The feature difference of useful signal and noise in bridge health monitoring system is analyzed and summarized, and the corresponding suggestions are presented. With the concept comparison of noise and measure error in bridge health monitoring, the procedure of noise reduction is proposed and analysis of noise features especially the difference to useful signal is carried out in detail.
3. Research on whitening process of non-white noise in bridge health monitoring is proceeded. On the basis of various types of non-white noise features and modern signal theories such as HHT, wavelet transform and Chebyshev filter, industrial noise whitening process based on EMD parameter detection and1/f noise wavelet threshold whitening method based on frequency range are proposed, whose applicability and effectiveness are verified by numerical simulation experiments.
4. Research on white noise reduction in bridge health monitoring is carried out. In allusion to the features of bridge health monitoring sampling signal and shortages of traditional algorithms, a new algorithm named EMD wavelet correlation noise reduction algorithm is proposed based on the advantages of EMD, wavelet transform and correlation detection. Simulation experiments demonstrate that the proposed algorithm has weak influence by each parameter, and it is very suitable for noise reduction of sampling signal with low frequency, weak signal to noise ratio and abundant details.
5. The effect and rate improvement of structure damage identification on different conditions is proved by the research on structure damage identification using integrated noise reduction algorithm, which is established by combination of non-white noise and white noise reduction method. Noise reduction in bridge health monitoring was carried out by the proposed algorithm and proved by the finite element simulation experiment. Effect and rate comparison of damage identification before and after integrated noise reduction is proceeded using traditional damage alarming algorithm based on wavelet packet energy and proposed damage localization algorithm based on NExT wavelet packet energy, the results of finite element simulation experiment confirm the applicability and effectiveness of integrated noise reduction algorithm in damage identification, and the effect of structure damage alarming and localization using de-noised signals increases significantly.
引文
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