铁磁性平板腐蚀缺陷多通道漏磁信号的反演与重构
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摘要
随着我国战略原油体系的建立和经济的发展,储罐的数量和容量增加很快,随之而来的安全问题也受到了人们的日益关注。储罐最容易腐蚀的部位是储罐底板,当今储罐底板多由铁磁性材料制造而成,其常用的无损检测方法有超声检测、渗透检测、磁粉检测等,其中漏磁检测凭借其低成本、高灵敏度等优点在储罐底板腐蚀缺陷大面积普查中得到了广泛的应用。
漏磁检测方法对检测结果做出评价的依据是漏磁信号,目前对漏磁信号的研究可分为正、逆两个方向,正问题是指由缺陷到信号的研究,它往往是针对一定尺寸的缺陷,来研究其产生的漏磁信号。逆问题则是指由缺陷的信号反推缺陷的形状,也就是所谓的反演。本文以铁磁性平板的漏磁信号为基础,分别对缺陷识别、缺陷定位、缺陷量化等方面进行了深入的研究,提出一种基于多通道的铁磁性平板腐蚀缺陷量化反演方法,实现了缺陷检测的可视化。
针对目前缺陷漏磁场有限元分析没有考虑到磁化结构移动的影响,模拟结果与实测信号有一定的误差,本文提出了磁化结构在移动状态下的缺陷漏磁场分析方法。实验结果表明,该方法的计算结果更加贴近于漏磁场的实际空间分布。根据有限元仿真结果和实测漏磁场空间分布,对缺陷几何尺寸、缺陷形状与漏磁场之间的关系进行了定性与定量分析。采用小波分析和EMD分解相结合的信号去噪方法,对漏磁信号进行去噪处理。采用小波多尺度分析方法,对缺陷漏磁信号进行研究,使原来单一的时域漏磁信号分解为系列不同频率尺度下的信号。以漏磁场的垂直分量为研究对象对缺陷漏磁信号进行了时域、频域及频谱分析,并选取了用于描述缺陷波形的特征量。针对实验采集到的漏磁信号噪声大、采集间隔大等问题,对其进行了去噪及圆滑处理,同时在相邻两路传感器之间及两个采集点之间分别进行了三次样条插值处理,构造出了一系列“虚拟传感器”及“虚拟采集点”,提高了特征提取的精度。通过实验方法与仿真方法建立了不同尺寸、不同形状的缺陷漏磁信号样本。根据建立的缺陷样本分别构造了用于识别缺陷类型和实现缺陷量化的神经网络,实现了腐蚀缺陷的识别与反演。最后针对缺陷三维轮廓重构问题提出了建立缺陷矩阵的反演思想,并根据神经网络的量化、识别结果实现了缺陷轮廓的可视化。
With the foundation of strategic oil system and development of economy, the number and capacity of tanks increase rapidly, and the security issues of tanks have also attracted people's increasing attention. The bottom of the tank is the most susceptible to corrode. Nowadays most of the tank bottoms are made of ferromagnetic materials. The usual nondestructive detection methods used for detecting tank bottom are ultrasonic testing, penetrant testing, magnetic particle testing, etc, among which magnetic flux leakage testing has got widely development in corrosion defects large area census of tank bottom, with the advantage of its low-cost, high sensitivity, etc.
In the field of magnetic flux leakage testing, magnetic leakage signal is the basis of test results evaluation, the study of magnetic leakage signal can be divided into forward and inverse directions. The forward problem refers to the study which is from the defect of a certain size to the magnetic leakage signals that defect generate. While the inverse problem refers to making a reverse deducing from the magnetic leakage signals to the shapes of defects, it is so-called inversion. This paper is based on magnetic flux leakage signals of the ferromagnetic floor, the defect recognition, defect location, disfigurement quantitative, etc were deeply studied, then a quantization inversion method based on multiple channels was raised, at last defect test visualization was realized.
Considering that conventional method for analysising defect magnetic flux leakage field ignors the factor of magnetic structure movement, there is a certain error between simulation results and measured signal, defect magnetic flux leakage field analysis method in the condition of magnetic structure movement was proposed in this paper. Experimental results show that the simulation results of this method are more approximative to the actual distribution of leakage magnetic field. According to the simulation results and the spatial distribution of measured magnetic flux leakage, qualitative and quantitative analysis between defect size, shape and its leakage magnetic field was done. Magnetic flux leakage signals were denoised by the combination of wavelet analysis and EMD. Magnetic flux leakage signals were broken down into different frequency signal scales by wavelet multiresolution analysis. The vertical component of leakage magnetic field is researched and characteristics which are used to describe the defect waveform were selected through time domain analysis, frequency domain analysis and time-frequency analysis. Low SNR and large collection interval are major problem of experimental data, so de-noising and smooth was done before feature extraction. At the same time cubic spline was applied between adjacent sensors and adjacent collection points, then a series of virtual collection points and sensors were created, so the accuracy of feature extraction was raised. Defect signal samples of different sizes and different shapes were constructed through experimental method and simulation method. Neural networks which were used to identify defect types and quantify defect size were established through defect signal samples, identification and inversion of corrosion defects were realized. The thought of creating defect matrix were proposed in order to reconstruct three-dimensional contour of defect. At last visualization of defect contour was realized through recognition results and quantifiable results of neural networks.
漏磁检测方法对检测结果做出评价的依据是漏磁信号,目前对漏磁信号的研究可分为正、逆两个方向,正问题是指由缺陷到信号的研究,它往往是针对一定尺寸的缺陷,来研究其产生的漏磁信号。逆问题则是指由缺陷的信号反推缺陷的形状,也就是所谓的反演。本文以铁磁性平板的漏磁信号为基础,分别对缺陷识别、缺陷定位、缺陷量化等方面进行了深入的研究,提出一种基于多通道的铁磁性平板腐蚀缺陷量化反演方法,实现了缺陷检测的可视化。
针对目前缺陷漏磁场有限元分析没有考虑到磁化结构移动的影响,模拟结果与实测信号有一定的误差,本文提出了磁化结构在移动状态下的缺陷漏磁场分析方法。实验结果表明,该方法的计算结果更加贴近于漏磁场的实际空间分布。根据有限元仿真结果和实测漏磁场空间分布,对缺陷几何尺寸、缺陷形状与漏磁场之间的关系进行了定性与定量分析。采用小波分析和EMD分解相结合的信号去噪方法,对漏磁信号进行去噪处理。采用小波多尺度分析方法,对缺陷漏磁信号进行研究,使原来单一的时域漏磁信号分解为系列不同频率尺度下的信号。以漏磁场的垂直分量为研究对象对缺陷漏磁信号进行了时域、频域及频谱分析,并选取了用于描述缺陷波形的特征量。针对实验采集到的漏磁信号噪声大、采集间隔大等问题,对其进行了去噪及圆滑处理,同时在相邻两路传感器之间及两个采集点之间分别进行了三次样条插值处理,构造出了一系列“虚拟传感器”及“虚拟采集点”,提高了特征提取的精度。通过实验方法与仿真方法建立了不同尺寸、不同形状的缺陷漏磁信号样本。根据建立的缺陷样本分别构造了用于识别缺陷类型和实现缺陷量化的神经网络,实现了腐蚀缺陷的识别与反演。最后针对缺陷三维轮廓重构问题提出了建立缺陷矩阵的反演思想,并根据神经网络的量化、识别结果实现了缺陷轮廓的可视化。
With the foundation of strategic oil system and development of economy, the number and capacity of tanks increase rapidly, and the security issues of tanks have also attracted people's increasing attention. The bottom of the tank is the most susceptible to corrode. Nowadays most of the tank bottoms are made of ferromagnetic materials. The usual nondestructive detection methods used for detecting tank bottom are ultrasonic testing, penetrant testing, magnetic particle testing, etc, among which magnetic flux leakage testing has got widely development in corrosion defects large area census of tank bottom, with the advantage of its low-cost, high sensitivity, etc.
In the field of magnetic flux leakage testing, magnetic leakage signal is the basis of test results evaluation, the study of magnetic leakage signal can be divided into forward and inverse directions. The forward problem refers to the study which is from the defect of a certain size to the magnetic leakage signals that defect generate. While the inverse problem refers to making a reverse deducing from the magnetic leakage signals to the shapes of defects, it is so-called inversion. This paper is based on magnetic flux leakage signals of the ferromagnetic floor, the defect recognition, defect location, disfigurement quantitative, etc were deeply studied, then a quantization inversion method based on multiple channels was raised, at last defect test visualization was realized.
Considering that conventional method for analysising defect magnetic flux leakage field ignors the factor of magnetic structure movement, there is a certain error between simulation results and measured signal, defect magnetic flux leakage field analysis method in the condition of magnetic structure movement was proposed in this paper. Experimental results show that the simulation results of this method are more approximative to the actual distribution of leakage magnetic field. According to the simulation results and the spatial distribution of measured magnetic flux leakage, qualitative and quantitative analysis between defect size, shape and its leakage magnetic field was done. Magnetic flux leakage signals were denoised by the combination of wavelet analysis and EMD. Magnetic flux leakage signals were broken down into different frequency signal scales by wavelet multiresolution analysis. The vertical component of leakage magnetic field is researched and characteristics which are used to describe the defect waveform were selected through time domain analysis, frequency domain analysis and time-frequency analysis. Low SNR and large collection interval are major problem of experimental data, so de-noising and smooth was done before feature extraction. At the same time cubic spline was applied between adjacent sensors and adjacent collection points, then a series of virtual collection points and sensors were created, so the accuracy of feature extraction was raised. Defect signal samples of different sizes and different shapes were constructed through experimental method and simulation method. Neural networks which were used to identify defect types and quantify defect size were established through defect signal samples, identification and inversion of corrosion defects were realized. The thought of creating defect matrix were proposed in order to reconstruct three-dimensional contour of defect. At last visualization of defect contour was realized through recognition results and quantifiable results of neural networks.
引文
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