油气管道缺陷漏磁图像的小波压缩技术研究

针对漏磁信号的特点,提出了基于小波分析的缺陷漏磁图像压缩方案。该方案通过调整正交函数零极点位置来设计小波基函数,利用该函数对图像作小波变换,对获得的各级小波系数用更新的JPEG图像压缩表阈值量化,再用算术编码方案处理量化结果,从而得到压缩的图像。试验结果表明,当压缩比小于30%时,图像压缩引起的失真不会对缺陷分析产生影响。     

钢管漏磁在线检测技术的研究

介绍了钢管漏磁检测的基本原理，对钢管在线漏磁检测系统进行总体设计。分析了漏磁场理论模型和讨论了影响缺陷漏磁信号的一些因素及补偿方法，针对该系统的特点设计了高速数据采集板，借助Windows系统平台，在所研制硬件的基础上，采用多线编程和虚拟设备驱动技术编制了数据采集、分析、状态显示、实时控制等面向对象、多功能模块化的软件，详细叙述了漏磁信号数据分析的方法和过程。这种系统具有检测速度快、数据吞吐量大、效率高、钢管缺陷分辨率高等特点。     

高速钢轨缺陷的漏磁检测及反演

为保障高速铁路行车安全,从铁磁性材料磁化的机理出发,分析漏磁检测的基本原理,介绍漏磁检测技术在钢轨缺陷检测中的应用研究,提出高速钢轨缺陷的漏磁检测方法。采用有限元法建立缺陷漏磁检测模型,分析缺陷漏磁场Bx、By和Bz分量的特点,并针对高速钢轨漏磁检测中缺陷提取相应的特征参数,运用人工神经网络的方法实现漏磁检测缺陷的反演,取得较好的反演结果。     

海底管道缺陷漏磁检测器数据采集系统研发

针对海底管道内可利用空间小、运行工况恶劣的实际情况,研发一款海底管道缺陷漏磁检测器数据采集系统.该系统选用ARM实现对各功能单元的控制,并以FPGA结合A/D采样芯片进行多路漏磁检测信号的并行采集;通过对各功能单元的一体化和集成设计,在确保能准确有效采集和存储缺陷漏磁信号数据的前提下,实现系统结构的整合及简化.牵拉试验和检测工程应用结果表明:该数据采集系统运行稳定,采集、处理缺陷漏磁检测数据质量高,达到设计要求,完全满足海底油气管道缺陷漏磁检测工程应用的技术要求.     

ERW管焊缝缺陷漏磁检测方法可行性分析

介绍了现有由轴向磁化和周向磁化检测技术组合构成的相对螺旋扫查式钢管漏磁检测方法,分析了该方法对ERW管焊缝缺陷检测的困难所在:相对螺旋扫查方式所形成的周期性焊缝信号掩盖了其上的缺陷信号,并且在漏磁叠加场的根源上难以区分.针对此问题,提出一种单一轴向磁化下的轴向直线扫查式ERW管焊缝漏磁检测方法,并通过实验和有限元法对其可行性进行了论证分析,在发现单一轴向磁化下钢管纵向伤可形成漏磁场并被观察出的基础上,对ERW管焊缝上最难以检出的人工纵向伤进行检测试验,最终表明:ERW管焊缝缺陷在单一轴向磁化下的漏磁检测方法具有可行性.     

基于混沌优化的板形信号模式识别的研究

针对传统的基于最小二乘法板形信号模式识别方法抗千扰能力差、精度低，神经网络识别方法在实际应用中效果不佳的问题，通过对板形信号和板形识别数学模型的分析，首先将板形信号模式识别过程转化为函数的优化问题。为提高板形信号识别的精度和速度，以勒让德正交多项式作为板形缺陷的基模式，用模糊识别理论与混沌优化方法对该函数进行优化求解。采用模糊理论作为初步识别，用以降低混沌优化的求解维数和缩小搜索空间，借助梯度下降法的思想对混沌优化的局部搜索能力进行改善，从而进一步提高了混沌优化对板形信号模式识别的识别速度和精度。     

基于径向基函数神经网络的滚动轴承故障模式的识别

径向基函数（RBF）神经网络是一种3层前馈性神经网络，它具有较强的函数逼近能力和分类能力。鉴于径向基函数神经网络的优点，在对滚动轴承振动信号特征分析的基础上，提出了采用时序方法对其建立AR模型，利用AR模型参数建立径向基函数神经网络，并用该网络对滚动轴承的故障模式进行了识别。理论和试验证明了该方法的有效性，且具有较高的识别精度。     

基于3/2维谱的螺栓松动非线性检测及定位

提出基于3/2维谱分析的螺栓松动非线性检测及定位方法,用多尺度法分析螺栓松动产生非线性相位耦合的机理,研究3/2维谱分析处理信号的过程并分析其识别非线性二次谐波原理,以铝板上螺栓结构为实验对象,利用粘贴在铝板表面的压电作动/传感元件进行实验,对结构响应信号进行3/2谱分析,有效判断螺栓的连接状态;为实现松动螺栓的定位,定义螺栓松动的非线性指标,引入径向基插值函数,实验获得粘贴在铝板上压电列阵响应信号的非线性指标,利用径向基插值函数拟合损伤定位图像。实验结果表明,该方法能有效检测螺栓松动非线性,实现松动螺栓定位。     

基于数学形态学滤波的漏磁信号预处理方法研究

漏磁检测是一种广泛应用于铁磁材料表面裂纹检测的磁性无损检测技术,漏磁信号的质量直接关系到裂纹定量识别的准确性和精度。针对漏磁信号的噪声特性,提出一种基于数学形态学滤波的漏磁信号预处理方法,即利用改进的中值滤波法剔除信号中的奇异点,采用多项式拟合法消除信号趋势项,使用形态滤波法对漏磁信号进行消噪处理。结果表明:该方法对漏磁信号中的干扰噪声具有较强的抑制能力,不仅剔除了漏磁信号中的干扰噪声,而且完整地保留原始信号的具体细节,提高降噪速度。     

油气管道腐蚀缺陷分类识别技术研究

缺陷准确量化是管道漏磁检测领域中长期存在的一个难点,而对缺陷进行科学的分类是实现准确量化的重要前提。针对不同形态的缺陷,分析其特征参数对漏磁信号的影响因素,建立用于缺陷分类的BP神经网络模型,设计改进的Levenberg-Marquardt算法用于网络训练,并利用Ansoft Maxwell 3D建立仿真缺陷数据作为样本进行测试。结果表明:改进后的神经网络系统可实现对缺陷的有效分类,改善传统算法分类精度低、误差大的缺点,收敛速度大幅度提高。该方法已成功应用于大型油气田软件工程领域,为实现缺陷准确量化提供基础和依据。     

Magnetization Time Lag Caused by Eddy Currents and Its Influence on High-Speed Magnetic Flux Leakage Testing

In high-speed magnetic flux leakage (MFL) testing, the tested workpieces pass rapidly through magnetizers. Thus, the magnetization time for workpieces is short. Because of the eddy current effect, the magnetic field inside the workpieces cannot instantly reach equilibrium, and if the magnetizing time is insufficient for the field to reach equilibrium, the MFL signals will be changed because of incomplete magnetization. In this article, the magnetization time lag caused by eddy currents and the influence of this lag on high-speed MFL testing is investigated. The time required for magnetic field to reach equilibrium in specimens, including steel bars and pipes, is obtained by theoretical calculations, finite element simulations, and experiments. The results indicate that the time required for a magnetic field inside a specimen to reach equilibrium is in the range of 50–100 ms. Using conventional magnetizers, the defect signals at testing speed of 10 m/s change because the workpiece reaches the detection zone before the magnetic field inside reaches the stable state. A simple solution is to increase the axial length of the magnetizing coil. After this procedure, signals obtained at 0.1 m/s and 10 m/s are almost identical.     

Simulation and Analysis of 3-D Magnetic Flux Leakage

In this paper, we present simulation results and analysis of 3-D magnetic flux leakage (MFL) signals due to the occurrence of a surface-breaking defect in a ferromagnetic specimen. The simulations and analysis are based on a magnetic dipole-based analytical model, presented in a previous paper. We exploit the tractability of the model and its amenability to simulation to analyze properties of the model as well as of the MFL fields it predicts, such as scale-invariance, effect of lift-off and defect shape, the utility of the tangential MFL component, and the sensitivity of MFL fields to parameters. The simulations and analysis show that the tangential MFL component is indeed a potentially critical part of MFL testing. It is also shown that the MFL field of a defect varies drastically with lift-off. We also exploit the model to develop a lift-off compensation technique which enables the prediction of the size of the defect for a range of lift-off values.      

De-noising of Magnetic Flux Leakage Signals Based on Wavelet Filtering Method

ABSTRACT

To improve the accuracy of the magnetic flux leakage (MFL) nondestructive testing in practical applications, it is very significant and key to deal with the detected MFL signals. As for the de-noising process of the MFL signals, a multilevel filtering approach based on wavelet de-noising combined with median filtering is proposed. By analyzing and comparing the de-noising properties of three wavelet families, i.e., Daubechies wavelet, Coiflets wavelet, and Symlets wavelet, two wavelet bases with the best de-noising performance are recognized and selected, namely sym6 and sym8 (the Symlets wavelet functions of order 6 and 8). Then, a new cascaded filter is constructed by combining sym6 and sym8 wavelets and cascading the median filtering method. An experimental platform is established to carry out the MFL testing, through the de-noising process for the measured MFL signals, and the results indicate that the proposed improved algorithm integrates with the merits of wavelet de-noising and median filtering. Compared with the traditional wavelet de-noising, the improved algorithm can not only improve the signal-to-noise ratio (SNR), but also reduce the de-noising error, resulting in enhancing signal quality to facilitate subsequent defect recognition.     

Theoretical Analysis and Numerical Simulation of the Feasibility of Inspecting Nonferromagnetic Conductors by an MFL Testing Apparatus

ABSTRACT

It has been widely accepted that the magnetic flux leakage (MFL) testing system can be applied only to the inspection of ferromagnetic materials. The possibility of using the MFL testing apparatus to inspect nonferromagnetic metals is discussed in this article. According to Faraday’s law of induction, eddy current rises in the conductor passing through the MFL magnetizer. The perturbation of eddy current and its corresponding magnetic field caused by defects are theoretically analyzed. Then, the finite element method is carried out to verify the theoretical analyses and extract the perturbed magnetic field signals. Furthermore, the influences of specimen conductivity and moving velocity on the detection signal amplitude are also simulated. The results show that the nonferromagnetic conductors are possible to be inspected by the MFL apparatus, and higher conductivity or inspection speed will facilitate the inspection.     

Signal processing for in-line inspection of gas transmission pipelines

Gas transmission pipelines in the United States are primarily inspected using the magnetic flux leakage (MFL) nondestructive evaluation (NDE) technique. However, accurate analysis of the NDE signals in terms of the underlying defects requires a thorough knowledge of various operational parameters such as B-H characteristics of the pipe wall, the velocity of the scanning tool, etc. In certain situations, information about such operational parameters is either absent or hard to obtain. Appropriate signal processing techniques can be applied to the raw MFL signals to ensure that defect characterization is possible in spite of local variations in the test situation. This paper presents two such signal processing methods—one, to compensate the MFL signal for variations in pipe-material grade, and the other to remove the effects of signal distortion that occur due to the velocity of the scanning device.     

Signal Processing for In-Line Inspection of Gas Transmission Pipelines

Abstract

Gas transmission pipelines in the United States are primarily inspected using the magnetic flux leakage (MFL) nondestructive evaluation (NDE) technique. However, accurate analysis of the NDE signals in terms of the underlying defects requires a thorough knowledge of various operational parameters such as B-H characteristics of the pipe wall, the velocity of the scanning tool, etc. In certain situations, information about such operational parameters is either absent or hard to obtain. Appropriate signal processing techniques can be applied to the raw MFL signals to ensure that defect characterization is possible in spite of local variations in the test situation. This paper presents two such signal processing methods–one, to compensate the MFL signal for variations in pipe-material grade, and the other to remove the effects of signal distortion that occur due to the velocity of the scanning device.     

A Novel Non-destructive Testing Method by Measuring the Change Rate of Magnetic Flux Leakage

The application of magnetic sensors in the traditional magnetic flux leakage (MFL) technique has a significant influence on the detection results. The sensor is typically used to directly measure the amplitude of the magnetic leakage flux intensity as the detection signal. In view of noise effects on the detection result and the subsequent misinterpretation of defect signals, a new non-destructive testing method is proposed. The proposed method intends to measure the magnetic flux change rate using two sensors. A mathematical model is first established to present the principle of the change rate measurement. Based on the magnetic dipole theory, it is inferred that the new method is applicable and sensitive to the detection and location of defects. Moreover, this method is advantageous as it inhibits the interference of MFL noises such as the distension noise, background noise, and vibration noise. The model predictions are then verified by a series of simulations. Finally, an experimental platform is set up to practically detect the defect of a steel plate, and the results agree with the demonstrations and simulations.     

Electromagnetic NDE signal inversion by function-approximation neural networks

In the magnetic flux leakage (MFL) method of nondestructive testing commonly used to inspect ferromagnetic materials, a crucial problem is signal inversion, wherein the defect profiles must be recovered from measured signals. This paper proposes a neural-network-based inversion algorithm to solve the problem. Neural networks (radial-basis function and wavelet-basis function) are first trained to approximate the mapping from the signal to the defect space. The trained networks are then used iteratively in the algorithm to estimate the profile, given the measurement signal. The paper presents the results of applying the algorithm to simulated MFL data.     

Effect of stress concentration on magnetic flux leakage signals from blind-hole defects in stressed pipeline steel

Stress-dependent magnetic flux leakage (MFL) signals of the normal surface component (radial) MFL signal from blind-hole defects in pipeline steel were investigated. Three different stress rigs with uniaxial stress and field configurations were used. A double-peak feature in the MFL signal was defined quantitatively by a saddle amplitude, which was taken as the difference between the average of the double peaks and the corresponding saddle point. Results indicated that the saddle amplitude increased linearly with increasing tensile surface stress and decreased, or did not exist, for increasing compressive surface stress. Finite-element calculations indicated that stress concentration also increased with increasing defect depth. The measurements and analysis demonstrate that the stress-dependent saddle amplitude behavior in the radial MFL signal is associated with surface-stress concentrations near the blind-hole defects.     

Improved FEM model for defect-shape construction from MFL signal by using genetic algorithm

In-line inspection of ferromagnetic gas or oil pipe lines having pipe wall defects is typically accomplished using magnetic flux leakage (MFL) technique. An efficient modelling and computational scheme for forward model, during the process of solving inverse problems in magnetostatic non-destructive evaluation using finite-element method is presented. The shape, size and place of defect are determined considering the nonlinearity of the pipe material using genetic algorithm as the optimisation technique. It is shown that the reduced model improves the FE computations significantly. The methodology for construction of defect shapes from particular MFL signals has been explained