强化磁激励条件下磁记忆检测试验研究北大核心

对含预制缺陷的16MnR钢平板试件进行拉-拉疲劳试验,试验过程中采用励磁装置进行稳恒弱磁激励,通过巨磁阻传感器采集试件表面应力集中区域的漏磁场信号。结果表明,在弱磁激励和载荷作用停止时测得的漏磁场切向分量仍可以认为是一种广义的磁记忆信号,适当加强外磁场激励可以突出、强化磁记忆信号,提高检测系统的灵敏度,使检测效果得到改善;漏磁场切向分量在试件应力集中处出现最大值,其值随循环次数增加而增大,且在疲劳的不同阶段幅值变化率不同。     

基于波形特征提取的管道腐蚀缺陷量化研究

针对当前管道腐蚀缺陷检测的研究现状，提出采用缺陷漏磁场的切向分量作为漏磁检测仪采样的原始信号，将管道内分布的96路传感器信号插值和周向展开显示，定义了缺陷漏磁信号波形的特征量，借助多元统计分析方法来定量评价缺陷外形的长、宽、深，实验得出该量化缺陷方法有效，精度在允许范围内。     

一种磁记忆检测定量分析的新方法

金属磁记忆检测技术是利用磁记忆效应对铁磁性材料的应力集中区进行无损检测的新方法.磁记忆效应的特征是应力集中部位表面的漏磁场的切向分量最大、法向分量存在过零现象.在目前的研究工作中,研究方法主要是以测量磁记忆法向信号过零点位置来判断应力集中,从而割裂了切向与法向之间内在的关系.探讨了采用法向切向联合检测的方法,利用一种新型的磁阻传感器HMC1002,设计组装了高精度的二维弱磁场测量传感器.通过对拉伸试件进行2种方法检测的对比实验,结果表明,此方法比单一的法向磁场分量过零值点的检测更有效,可望在磁记忆检测定量分析研究中有很好的实际应用价值.     

基于三维场测量的脉冲漏磁检测技术

漏磁检测广泛应用于铁磁性材料缺陷检测.脉冲漏磁检测技术是一种新型电磁无损检测技术.缺陷的定量评估是无损检测的重要步骤之一.运用有限元法对管道周向外壁缺陷的漏磁场进行瞬态分析,分析了缺陷脉冲漏磁场Bx、By和Bz三维分量的分布特点,最后给出了仿真结果和试验结果,试验结果和仿真有很好的一致性.试验结果表明:综合脉冲漏磁场的三维峰值扫描电压可以对缺陷的长度和宽度进行检测,而三维分量差分信号中的过零时间随缺陷的深度变化而变化,且不随缺陷宽度的改变而改变,由此可以检测缺陷的深度.三维脉冲漏磁检测系统使缺陷的定量检测有了可能.     

金属管道裂纹的磁记忆检测研究

提出采用裂纹区的应力场和应力-磁场理论来解释磁记忆检测原理,并通过采用磁偶极子模型来实现裂纹长度的定量化研究;对各种不同管道裂纹进行磁记忆检测,再对其磁记忆信号特征进行分析、研究。检测分析结果表明:裂纹尖端处应力集中且磁场出现最大值,磁场强度的法向分量也达最大值;而在裂纹中部附近,磁场强度的切向分量出现最大值,法向分量具有过零点特征。裂纹长度与磁记忆切向分量信号梯度峰值之间的距离和法向分量信号的波峰和波谷之间的距离近似成正比例关系。根据这一现象,给出了一个具有较好计算精度的确定裂纹长度的计算表达式,并用实验结果进行了验证。     

磁记忆二维检测的研究与工程应用

磁记忆检测作为早期诊断的新兴技术已受到国内外无损检测界广泛关注,但现有的磁记忆检测技术主要以单一方向上的漏磁场信号(法向分量)来进行判断,没有充分利用磁记忆检测信号的全部信息,容易出现漏检、误检等问题。针对该项检测技术的不足,介绍一种将磁记忆检测信号法向分量存在过零点和切向分量存在极大值的特征曲线联合应用的二维检测方法。该方法同时采集法向和切向的磁记忆信号,利用矢量合成法获取切向最大值,然后求取法向、切向分量一阶微分后合成的李萨如图,利用李萨如图的面积对应力状态进行分析,并将自主研制的磁记忆二维仪器应用到工程实践中。实践证明,该二维系统及检测方法能高效地对应力集中进行判定,可望成为实现磁记忆检测定量分析的新方法。     

双传感器差分峰值侦测的漏磁检测新方法

为了提高管道、储罐漏磁检测的准确度、灵敏度,同时抑制噪声干扰,提出了一种双传感器差分峰值侦测的漏磁检测新方法。首先,对实际漏磁检测过程存在的背景噪声进行分析,研究了检测器振动、磁隙变化、磁轭泄漏等噪声源的共模特性;其次,利用缺陷漏磁场的分布特点构造了一种新型的差分侦测结构,该结构既可对噪声进行共模抑制,又可实现缺陷特征信号的差模提取;再次,依托磁偶极子模型,对所提方法的机理进行了分析,并讨论实现该方法所需要的约束条件及其检测灵敏度特性;最后,开展了仿真和物理实验,实验结果表明,该方法的噪声仅为传统X分量检测的5%,Y分量检测的26%,且检测敏感度提高了2倍。该方法为优化漏磁检测器结构、提高检测的可靠性及灵敏性提供了一个新的思路。     

基于模块组合式阵列漏磁传感器的微细裂纹定量化研究

采用自行研制的模块组合式漏磁传感器实现了微细裂纹的定量化研究。漏磁传感器核心器件为AMR磁敏元件,基于漏磁信号切向分量,提取与人工预制裂纹尺寸相关的一组特征量;利用三次样条法拟合实验数据,构建虚拟实验样本,采用GA-BP神经网络对样本库进行训练,并定量识别微细裂纹。研究表明,文中所提技术思路能有效实现微细裂纹的定量化评估。     

管道缺陷漏磁检测信号的仿真

漏磁检测是管道无损检测的常用方法,也是最有效方法之一.在检测管道的过程中,对于不同的缺陷会检测到不同的漏磁信号.通过建立管道检测的实体模型,对管道斜向裂纹缺陷所产生的漏磁信号运用ANSYS有限元软件进行模拟仿真,从仿真信号中的磁通密度纵横向矢量图中,直观地显示了漏磁场附近的特点,找到缺陷轮廓及参数.利用有限元可以分析出,缺陷漏磁场的峰值会随着裂纹的倾斜角度、宽度、深度、提离值的大小变化而变化,可以方便地建立大量大小不一形状不同的缺陷样本库,为缺陷的识别提供依据并为定量分析做准备,为进一步对漏磁场的研究打下基础.     

高速漏磁检测过程中管道内外壁缺陷定位方法研究

管道内外壁缺陷的有效区分是对缺陷进行有效量化的前提,提出一种基于动生涡流的高速漏磁检测过程中管道内外壁缺陷的定位区分方法,利用涡流磁场与外磁场的耦合作用时内外壁磁场信号的变化差异特征区分缺陷位置。首先建立高速漏磁检测数学模型,分析了涡流分布特点以及涡流磁场与外磁场耦合作用规律,利用有限元方法计算分析不同位置时,耦合作用规律对管道内外壁磁化状态影响及内外壁缺陷漏磁场信号差异特征;设计高速漏磁检测实验平台,对不同运行速度、不同检测位置处钢管内外壁缺陷区分效果进行实验研究。结果表明,接近磁化线圈位置时,管壁内产生的涡流磁场方向与管道外壁磁场方向相同、与管道内壁磁场方向相反,在离开磁化线圈位置时,涡流磁场方向与管道外壁磁场方向相反、与管道内壁磁场方向相同;不同检测位置处,管壁磁场变化规律相反,且速度越快,磁化状态影响受影响程度越大,内外壁漏磁场信号差异特征越明显,高速检测时可有效对管道内外壁缺陷进行定位区分,实验结果和理论分析具有很好的一致性。     

A new denoising algorithm for MFL data obtained from seamless pipeline inspection

The magnetic flux leakage (MFL) nondestructive evaluation technique is used extensively for in-service inspection of gas and oil pipelines. Unfortunately, the MFL data obtained from seamless pipeline inspection is usually contaminated by various sources of noise, which considerably reduces the detectability of defect signals in MFL data. In this paper, a new denoising algorithm is presented for removing seamless pipe noise (SPN) and system noise contained in MFL data. The algorithm first utilizes the new wavelet domain adaptive filtering method proposed by combining wavelet transform with the adaptive filtering technique to remove SPN contained in MFL data and then exploits the coefficient denoising approach with wavelet transform to cancel the system noise in the output of the wavelet domain adaptive SPN cancellation system. Theoretical analysis shows that the proposed denoising algorithm has a better overall performance than the existing denoising algorithm. Results of application of the proposed algorithm to MFL data from field tests are presented to demonstrate the performance of the proposed algorithm compared with the existing denoising algorithm. The text was submitted by the author in English.     

Signal processing of magnetic flux leakage surface flaw inspect in pipeline steel

The magnetic flux leakage (MFL) method has established itself as the most widely used inline inspection technique for the evaluation of pipelines. The MFL signal is usually contaminated by various noise sources This paper presents the empirical mode decomposition (EMD) for enhancing signal-to-noise ratio. Experiments are conducted on the pipeline steel samples with different depth artificial defects. The MFL signal is decomposed into several intrinsic mode functions by EMD. Some modes are selected to reconstruct a new signal considering their frequencies and energy. The reconstructed signal has a better signal-to-noise ration. The results show that EMD technology is efficient in eliminating noise in the MFL signal. The article is published in the original. Published in Russian in Defektoskopiya, 2008, Vol. 44, No. 12, pp. 72–79.     

Defects detection of gas pipeline near the welds based on self quotient image and discrete cosine transform

The magnetic flux leakage (MFL) inspection has been widely used in the inline inspection for the evaluation of steel pipelines and plates. In this paper, a defect detection algorithm based on the MFL inspection is proposed for detecting defects near the welds. The defect in this paper means some deformations and deterioration of steel pipes because of corrosions and cracks by humidity and pressure after gas pipes were buried and it doesn’t mean bad welding. The MFL signal of the defects near the welds is worse than that of the far-away from the welds and has low signal-to-noise ratio (SNR). In this paper, the MFL signal of the defects near the welds is enhanced by using the Self Quotient Image (SQI) in this paper and the position of the defects is detected after applying the Discrete Cosine Transform (DCT). Experiments are conducted to demonstrate the accuracy of the proposed defect detection algorithm for the artificial defects carved on the pipes at the pipeline simulation facility (PSF) and the results show that the proposed algorithm can successfully detect the position of the defects on the pipes near the welds.     

在线压缩感知方法及其在漏磁检测中的应用

以长距离油管的漏磁检测系统为研究对象,研究了漏磁检测数据的在线压缩算法。针对嵌入式在线工作环境下,传统的数据压缩方法难以应用的问题,引入压缩感知(CS)理论,提出了漏磁检测数据在线CS压缩方法。确定了小波基作为漏磁信号的最佳稀疏表示基,并推导了小波稀疏基矩阵的数学表达公式;提出Welch界和PRP共轭梯度算法的测量矩阵优化算法;提出了漏磁检测数据的重要数据段筛选方法,极大地减少了数据存储量。仿真试验证明了所提出在线压缩算法极大地减少了在线环境压缩编码的运算复杂度,具有简单迅速、压缩比高、重构精度高等优点,符合漏磁检测数据在线压缩的实际要求。     

供水管道泄漏定位中基于互谱的时延估计

供水管道泄漏时产生泄漏声波，根据泄漏声波到达安装在管道上的2个传感器的时间差可以估计泄漏位置。减小时延估计的方差必须对信号滤波以提高信噪比，但泄漏信号所在的频带是不能预先确知的。互谱相位谱和相干函数在频域上反映了2个信号的相关性，根据它们估计泄漏信号的频带、设定滤波器截止频率，在较低信噪比的情况下提高滤波效果及检测结果的置信度。实际检测结果表明，该方法减小了泄漏定位的误差。     

经验模态分解在管道缺陷漏磁检测信号处理中的应用研究

针对管道缺陷漏磁检测信号中存在严重噪声干扰的问题，将经验模态分解方法用于漏磁检测信号的噪声分离和有效信号提取，对实际测试的与输油管道材质相同且具有人为模拟缺陷的漏磁信号进行处理，结果表明，该方法可以很好地抑制噪声从而得到清晰的、表征缺陷特征的有用信号，达到与小波变换相同的处理效果，同时克服了小波方法中基函数选择困难的问题。     

Numerical simulation and experiments of magnetic flux leakage inspection in pipeline steel

The magnetic flux leakage (MFL) method is currently the most commonly used pipeline inspection technique. In this paper, numerical simulation and experimental investigation on defect inspection in pipeline steel using MFL were carried out. In theoretical analysis, typical three-dimensional (3D) defects were accurately modeled and detailed MFL signals in the test surface were calculated by 3D finite element method (FEM). To confirm the 3D FEM results, different artificial defects were made and the MFL experiments were performed. The experimental study demonstrated that the results were agreement with the 3D FEM result. The results show that the 3D FEM is an effective analysis method for pipeline steel MFL inspection. This paper was recommended for publication in revised form by Associate Editor Jooho Choi Xun-Bo Li received his M.S. and Ph.D degree from University of Electronic Science and Technology of China in 1991 and 2001, respectively. Dr. Li is currently a Professor at the School of Mechatronics Engineering, University of Electronic Science and Technology of China in Chengdu, P.R. China. Dr. Li’s research interests are in the area of Mechatronics, measurement and NDT.     

公共场所异常声源定位中时延估计方法研究

HB(Hassab-Boucher)加权广义互相关(generalized cross correlation based on HB weighted function,GCC-HB)是常用的时延估计方法,在环境为弱高斯噪声情况下,可获得较为精确的时延估计值用于声源定位。通过分析认为,通常公共场所异常声音是一种短时信号,背景噪声主要为粉红噪声与脉冲噪声,符合分数低阶α稳定分布(fractional lower order alpha-stable,FLOA)。在此背景噪声的低信噪比环境下,GCC-HB方法的时延估计性能急剧下降。为此,提出基于反正切变换的改进GCC-HB的时延估计方法(improved GCC-HB method based on arc tangent transform,ATAN-IHB)。该方法首先对加噪信号采用反正切变换抑制噪声中尖峰脉冲的影响,然后结合每帧的信噪比对HB加权函数进行改变,并由多帧HB加权后的峰值确定出时延估计值。理论分析和计算机仿真结果表明,所提出的方法即使在低信噪比的环境下,也可以获得比较满意的时延估计值,具有一定的实用性价值。     

Noise Variance Estimation Using Image Noise Cross‐Correlation Model on SEM Images

A number of techniques have been proposed during the last three decades for noise variance and signal‐to‐noise ratio (SNR) estimation in digital images. While some methods have shown reliability and accuracy in SNR and noise variance estimations, other methods are dependent on the nature of the images and perform well on a limited number of image types. In this article, we prove the accuracy and the efficiency of the image noise cross‐correlation estimation model, vs. other existing estimators, when applied to different types of scanning electron microscope images. SCANNING 35: 205‐212, 2013. © 2012 Wiley Periodicals, Inc.