X射线探测焊缝及机械损伤方法概述----中英文翻译

Original text：
X-RAY DETECTS WELDS AND MECHANICAL
STRUCTURE DAMAGE MATHoDSfUMMARIZE
The moving small object detection in image is always a difficult problem in field of image processing, which applies in many fields, such as industrial detection and medical detection. The defects, such as blowholes and incomplete penetration, occasionally appear in the welding process. These defects can affect the quality and the security of products. Therefore, defects detection in welding seam is extremely important. Now, the on-line detection of defects in the weld is still done by human interpreter. However, this process is subjective, inconsistent, labor intensive and fatigue of interpreter. It is desirable to find an effective automatic defects detection method to assist human interpreter in evaluating the quality of weld and to make the on-line detection objective, standard and intelligent. Our research is based on this.
We have studied the automatic defects detection in the weld seam and mainly done the following research :(1) There is much redundant background information for the defects detection in the image. Therefore we use an automatically abstracting method of weld area based on the auto- adapted threshold segmentation. This method can reduce the computation and increase the precision. (2) The SUSAN algorithm has
good anti-noise ability, which can recognize the image edge very well. So we have studied a defects detection method based on SUSAN algorithm, which associated with the morphology operation. The results indicate that this method is effective.(3) Wavelet analysis method has a very good localization characteristic, which can focus on the arbitrary detail of the analyzed object. Therefore, we studied a method using wavelet decomposition to get the shape and position information of the defects. Then we use the wiener filter and morphology method to complete the detection.
The automatic flaw detection of welded tubes is one of the most important steps to ensure the quality of the tubes. Nondestructive inspection on welding seam of tube is required in the tube production, and real time X-Rayradiography inspection is an effective means. Along with continuous improvement of the productive ratio, the demand for the automatic inspection to the welding seam becomes more and more pressing, so implementation of the automatic inspection possesses important significance on both theory and reality. Wavelet transform is a powerful tool in the signal and image processing, and its fundamental theory has been formed. From the view of engineering applications, however, the wavelet transform is still in the elementary stage, the further Researches are required for the practical uses. In this thesis, we concentrate mainly on using wavelet analysis for welding seam image
processing and recognition, and some related techniques are developed.
For constructing welding seam positioning and detection control system, the multiple computers configuration for weld seam image recognition is proposed. The system adopts the architecture in which multiple CPUs process parallels under the control of the master IPC computer. The system can perform storing the weld seam images, positioning, flaws recognizing and quality prejudging. The Watch-Dog interface card is successfully developed; it can improve the system reliability by redundancies technique of saving breakpoint data and restoring them.
The hardware supporting the system makes use of the high speed digital signal processor TMS320C30 from Tax ax Instruments Company. The frame grabber can capture 25 frames of welding seam image per second continuously and make it possible to fulfill the real time welding seam image processing Andre cognition. The one kind of improved FWT (Fast Wavelet Transform) algorithm for a finite sequence is proposed after studying theory of mustier solution analysis
and analyzing technical characteristics of DSP. The implementation of the periodic extension of the FWT on DSP is described in detail and the corresponding FWT assembly code is described for the DSPTMS320C3X series.
This dissertation suggests scheme of image demonizing based on
two-dimensional discrete wavelet transform. The demonizing algorithm is described with some operators. By threshold the wavelet transform coefficients9of noisy images, the original image can be reconstructed correctly. Different threshold selections and threshold methods are discussed. The new robust local threshold scheme is proposed. Quantifying the performance of image demonizing schemes by using the mean square error, the performance of the robust local threshold scheme is demonstrated and is compared with the universal threshold scheme. The experiment shows that image demonizing using the robust local threshold performs better than that using the universal threshold.
In order to improve the accuracy and the real time performance of edge detection, a method need to be found to match the detection of low contrast blurred welding seam image. This dissertation analyzed the main sources of noise as well as the different characteristics of noise and signal under wavelet transform, and proposed a Moultrie solution edge detection method based on wavelet transform.
The experimental results show the effect of this algorithm is advantageous over that of traditional edge detection algorithm.
The geometrical relation of elliptic imaging is studied for welding seam image of the butt welds in straight tubes. The region model of welding seam image is proposed, It furnishes a evidence theory to further process to welding seam image. Combining with the region model, a
model-based adaptive target segmentation algorithm is proposed. One basis of the algorithm is Otsu's discriminates criterion. The adaptive target segmentation of welding seam image is realized. The effect of target image segmentation is quite well.
The difficult problem of target flaw automatic recognition in welding seam image is analyzed. Using for reference the consciousness organizing process of the human vision system, a knowledge-based target recognition algorithm with mufti-feature fusion, mufti-window architecture and mustier solution is presented. With the help of certain prior knowledge, criteria and means of artificial intelligence, target flaws are extracted and recognized quite well. It is a Prospecting intelligent recognition algorithm.
The fast feature extraction algorithm for target geometrical feature is proposed. This algorithm is different from usual feature extraction methods which first need to change a gray image into binary image. The algorithms proposed get the feature of a image in the gray image directly. Using this algorithm can fast extract features of target flaws in welding seam image.
All kinds of mechanical devices and structure tend to become large-scale and high efficient with the industry developing and progress of science and technology. The mechanical devices and structure become very complex to meet the need of industry. The structure or devices are
damaged is couldn,t avoided during working under complex load and working for a long time. The loss caused by crash, fatigue, eroding and wear is about 6%一8% of GDP of USA and Japan. In our country, accident number of structural damage is 10 times as many as that in, intense industrialization country in eighties of last century. In 1986, the loss is 12 hundred million $ caused by the space shuttle named ,v challengerπ of U.S.A, crashed. In 1985, the accident cause of joint of electromotor set of Datong power plane crashed, In 1988, the accident cause of main beam of electromotor set of Qianlong power plane crashed, those accident cause of loss near 1 hundred million RMB. In our country, 6 serious accidents ware been caused by rotor of over 50Mw electromotor set damaged badly during 1984 to 1991 .Therefore, the study the theory and technique about large scale and complex mechanical devices and structure online inspect and early fault diagnosis is urgent task. Especially, how to detect the fault of structure as early as possible is engineers most want to do. But it is very difficult that faint signal produced by early fault is recognized.
The research reports of our country and overseas show that at present the study of structure damage inspect by vibration characteristics carry out most on the simple and symmetry structure just as beam and frame etc. and result is given based on finite element numeric calculation. But the structure and join of practical device is quite complex, it is
impossible modeling the practical device reliably by FE. So that realizing complex industrial device inspect online and early fault diagnosis by using vibrate testing technique is a problem that wants to be solved urgently.
Fault diagnosis technique is intercross subject. Especially, the base in theory of fault diagnosis of complex system is provided based on modern control theory, signal processing, pattern recognition, optimum method, decision-making and manual intelligent are developed rapidly.
Structure damage detection is a research project that has wide background of industrial application. But realizing large-scale and complex structure damage inspect online is depend on techniques such as development of accurate testing technique and signal processing method, based on getting to the best advantage mix model of structure damage detection, the sensors escape placed on structure reasonably and optimally The large-scale vibrating device as a researched object, the method structure damage detection is studied. In general NDT technique such ultrasonic test, ray test, magnetism applied in modeling offline mostly. The project test and scheme pervade test etc. are vibrate properties and structure damage characteristics from platitudinous: offline tests and analysis to structure as impotent information of online automatic fault diagnosis database. Then the method of few-testing, points modeling to
getting structure damage information has beer researched. Placing sensors reasonably and realizing large-scale an Complex structure damage inspect online are targets of this project.
The large-scale vibrating screen has been applied widely in coax industry and other industrial areas as a kind of important device. A: vibration mechanical device, it works very hardly and works in verb wretched environment so that the beam of screen is damaged easily Therefore, it is very important how to detect the fault of beam as early a possible to make the repair schedule reasonably and economically and to avoid the body hurt and device damage.
In this thesis, how can locate a damaged beam of screen is studied serially. The regulation of beam vibration characteristics change depend on damage degree of beam is found. Also the regulation of whole screen vibration characteristics change depend on damage degree of beam is found too. Based on deep research about beam vibration characteristics change regulation and whole screen vibration characteristics change regulation in series, we can get the optimum place to placed sensors for location which beam is damaged.
The target of the thesis is combine the on-line dynamically inspect screen for structure damage with accurately locate fault by acoustic emission technique.
The main content of this thesis consist of (1) Based on modal
parameters recognition of whole screen, get location of damaged substructure. (2) Locate fault of substructure accurately by acoustic emission technique. (3)Carry on research about finding a efficient way we can inspect screen for structure damage on-line.
These projects are done step by step. At first, free-free beam vibration characteristics are studied deeply. The first rank and second rank bending vibration modal shape of beam are abstracted as research objects. The study result is shown that the frequencies of FRF peak value drift toward lower frequency and the amplitudes of FRF peak value increase with the damage degree of beam. Then the first rank and second rank bending vibration characteristics of beam fixed on screen are studied. The change regulations of characteristics of FRF with beam damage are agreement to that of free-free beam. Therefore the damage information of beam can be gotten from FRE The wavelet packet analysis method and spectral analysis calculation method are employed in frequency response and transmissibility processing. The fault characteristics are abstracted. After then, the damaged beam has an effect on whole screen vibration characteristics are researched. From above work, the damaged beam of screen could be located from whole screen. Then the acoustic emission technique is used to locate fault of the damaged beam accurately. Because the too many sensors couldn't place on the working screen so that we must find limited positions to place sensors getting enough structure
damage information. At last, the method of the finding optimum places to placed sensors for location which beam is damaged is studied. The efficient way of optimum place to placed sensors is found.
In this thesis, the different spectral analysis calculation methods are employed in vibration signal processing to abstract fault characteristics. The processing result indicates that methods of vibration signal procession are efficiently.
This thesis provides some realizable ways to realize the on-line dynamically inspect screen for structure damage.
Translation:
χ射线探测焊缝及机械损伤方法概述
图像中运动小目标的检测一直是图像处理与分析领域中的难题，它涉及到很多领域，具有很广泛的研究价值和应用价值。

在工业探伤领域，由于焊接过程出现的各种问题,会导致焊缝中含有气孔和未焊透等缺陷，影响产品的质量和安全, 所以焊接图像中缺陷目标的检测十分重要。

目前X射线无损探伤系统主要采用人工方式进行在线检测与分析，而人工检测存在主观标准不一致、劳动强度大等缺点。

因此，急需要研究一种有效的缺陷自动检测方法来代替人工检测，从而使在线检测工作客观化、规范化和智能化。

本文的研究工作就是基于此而展开的。

本文探讨了焊缝图像中缺陷目标的自动检测方法，主要做了以下几个方面的研究:（1）针对X射线焊缝检测图像中存在大量与缺陷检测无关的背景冗余信息，采用了一种基于自适应闭值分割的焊缝区域的自动提取方法，以减少计算量，提高检测精度，取得了较好的效果。

（2）由于SUSAN算法具有良好的抗噪能力，对图像的边缘、角点能够很好的识别，所以本文研究了一种以SUSAN算法为基础的，焊缝缺陷自动检测算法，同时辅助以形态学去噪和填充等运算，取得了较好的效果。

（3）因为小波分析方法具有很好的局部化特性，它能对高频采取逐渐精细的时域或空域步长，从而可以聚焦到分析对象的任意细节。

所以研究了一种利用小波分解来得到缺陷目标的形状和位置信息、，并结合维纳滤波和形态学运算的焊缝缺陷检测方法，结果比较理想。

为了验证本文提出的两种算法的有效性，本文对在工厂实际得到的含有缺陷目标的焊接图像进行了检测，取得了较好的效果，证明了本文方法的可行性。

焊管缺陷的自动检测是保证钢管产品质量的重要环节。

在钢管生产过程中需要对焊管焊缝进行无损检测，X射线实时成象检测是一种比较有效的检测手段。

随着生产率的不断提高，对焊管焊缝的自动化X射线检测要求越来越迫切，实现焊管焊缝的自动化检测具有重要的理论意义和实际意义。

小波变换作为信号和图象处理的一种强有力的工具，其理论框架己基本形成，但从工程应用的角度，小波变换技术还处于初级阶段，还需进一步完善。

.本文主要研究小波分析技术如何用于焊缝图象处理与识别以及一些相关技术。

为建立焊管焊缝自动定位检测控制系统，提出了焊缝图象识别的多机系统结构方案，该系统采用多处理器并行处理的结构，并由上位机工PC协调控制管理。

系统能完成对焊缝图象的存贮、焊缝定位、缺陷识别和质量评定。

并成功地研制了基于工SA总线的WatCh-DOg接口板，使用冗余法进行断点数据存储和恢复，实现了系统的可靠运行。

硬件系统使用了Taxax仪器公司的高速信号处理器TMS320C30。

图象采集卡能每秒连续采集25帧焊缝图象，使得实时完成焊缝图象处理与识别成为可能。

在充分研究多分辨分析理论和分析信号处理器技术特点的基础上，针对DSP TMS320C3X 的特点，提出了一种有限序列的FWT（快速小波变换）的改进算法，详细阐述了信号处理器上FWT的周期性扩展的实现问题，用DSP TMS320C3X 汇编语言实现了改进的FWT算法。

通过对小波变换系数进行阂值处理，给出了一种基于二维离散小波变换的图像去噪方法并用算子的形式加以描述。

讨论了几种阂值选取方法和阂值策略，并提出了一种鲁棒局部闭值去噪法。

用均方差衡量去噪性能，实验表明用鲁棒局部闭值去噪法好于全局闭值去噪法。

为提高边缘检测的准确性和实时性，需要寻找一种适合于低对比度模糊焊缝图象边缘检测的快速方法。

本文分析了焊缝图象的主要噪声来源及噪声与信号在小波变换下呈现的不同特点，提出了一种基于小波变换的多分辨率边缘检测方法。

实验表明该算法的边缘检测效果明显优于经典的边缘检测方法。

针对具体的钢管直管对接焊缝图象，研究了其椭圆成象的几何关系，提出了焊缝图象区域模型，为进一步处理焊缝图象提供了理论依据。

提出了一
种模型基多分辨率图象自适应分割算法。

该算法以OtSU判别准则为基础，结合焊缝区域模型进行焊缝图象的自适应目标分割，具有较好的分割效果。

研究了在焊缝图象中目标缺陷的自动识别这一难题，在借鉴人类视觉系统知觉组织过程的基础上，提出了一种基于知识的多特征融合多窗口结构多分辨率目标识别算法。

该算法依据一定的先验知识和准则，辅以人工智能的手段，能够得到较为精确的目标识别结果，是一种极有前途的智能识别算法。

提出了一种几何特征快速提取算法，该算法改变了通常先图象二值化后提取目标参数特征的做法，而是直接对灰度图象进行目标参数特征提取。

使用本文提出的几何特征快速提取算法可以有效地实现缺陷目标的快速识别处理。

随着生产的发展与科学技术的进步，各类机械设备和结构向着大型、高效化发展，从而也使得这些机械设备和结构趋于复杂化。

复杂的承载条件与长时间的连续工作，导致设备结构的损伤不可避免。

因断裂、疲劳、腐蚀和磨损而造成的破坏，其损失达美、日等国家每
年国民经济总值的6%^,8%o而在我们国家20世纪80年代的结构损伤事故率比工业化国家高10倍，人员累计伤亡居国内劳动安全事故第二位。

1986年，美国的“挑战者，，号航天飞机失事损失高达12亿美元;苏联的切尔诺贝利核电站的
核泄漏事故，对整个地区的人员、
生态环境都是无法估量的损害。

1985年我国大同电厂一机组联轴器断裂事故、1988年秦岭电厂机组主轴断裂，造成的经济损失均近亿元，并严重影响华北和西北地区供电。

从1984年到1991年，我国50MW以上的汽轮发电机组转子严重损坏等重大事故就达6起。

因此，研究防止这类事故发生的根本途径一一大型复杂机械结构的健康状况监测与故障诊断（尤其是早期故障诊断）的理论和技术，实现结构损伤的早期识别，及时采取措施，防止损伤的发展，以保证这些系统安全、可靠、长寿命、高效率地运行成为紧迫的任务。

然而，对于大型的复杂机械结构，实现合理、优化地布置传感器，监测其运行结构的局部损伤，从中识别出损伤的结构件及损伤状态，进行早期故障预报是具有相当的难度的。

国内外
的研究报道显示，由于结构振动模态参数对不同损伤各有其敏感性，加之大型复杂结构振动模态识别技术发展的限制，以振动特性为参数对结构进行损伤检测的研究大多集中在诸如直梁、析架等简单对称结构，且多是基于有限元分析数值计算得出相应的结论。

而实际设备的构造及联接都是相当复杂的，无法实现可靠的有限元建模。

如何实现用振动测试技术对实际复杂上业结构的健康状况进行监测与故障的在线诊断还是一个急待解决的问题。

故障诊断技术具有很强的学科交叉性，尤其是现代控制理论，信号处理，模式识别、最优化方法、决策论、人工智能等的迅速发展，为解决复杂系统的故障诊断问题提供了理论基础，形成了许多具体的方法。

故障信号的特征提取为故障准确诊断的前提条件。

近三十年来，
各类机械设备基于实时监测（包括振动监测）的故障诊断技术的研究和应用，促进了故障信号处理和特征提取技术的发展。

这些技术包括时域信号波形分析和统计特征值提取;基于FFT分析的高斯平稳随机信号现代谱分析技术:自功率谱和互功率谱、高阶谱、倒谱、复倒谱以及谱嫡与极大嫡谱估计（也包括与之相对应的自相关与互相关函数、高阶自相关函数等时域分析）;非平稳信号的时频分析和多元统计分析等。

故障诊断技术还涉及到材料的选择、制造工艺、结构设计、断裂力学等多种学科和专业技术领域。

随着力学、材料科学、物理学、化学领域的学科交叉与发展，可从缺陷的背景和损伤、断裂机制来研究从材料变形、损伤到失效的全过程。

而计算机数据处理、模式识别的技术发展为与早期故障相关的微弱信号的捕捉和提取，提供了有利的手段。

结构损伤检测是一个具有广阔工程应用背景的研究课题，而大型复杂结构损伤检测的研究离实际应用还有距离，还有一许多问题需要在今后的研究中加以解决:如精确的测量信息处理技术的发展，以期获得更加精确的测量模态;基于吸收各种方法优点的混合模型的结构损伤检测方法的研究;大型设备的工况监测，传感器的合理布置与优化配置问题的具体应用;其它理论方法的引入，如模糊数学的应用以及子结构振动分析方法的应用等等。

本课题以大型振动机械为研究对象，进行结构损伤检测方法研究。

常规的无损检测方法，如超声波、射线以及磁力探伤与渗透法探伤等大多是用于生产过程中间环节的零件离线检测和设备检修，通常为静态检测。

本项目研究的基本思路是对大型振动机械进行多测点建
模，利用振动测试技术进行充分的离线试验和分析来获取被诊断结构的振动特性细节、故障机理及其特征，作为结构动力学本质特征库的先验知识与在线自动故障诊断信息库的重要内容;研究以结构的少测点获取结构损伤信息的建模方法，合理配置传感器，实现对大型振动
结构进行健康状况的在线监测。

通过振动特性的微小变化，发现与定位结构早期损伤，离线实现结构损伤细节的分析与估计。

上述思想是本项目研究的创新性思维。

课题具体内容包括:通过对整体结构的模态参数识别，判断损伤的结构;用声发射技术对损伤的结构进行损伤细节分析;研究对大型振动机械进行健康状况监测的方法。

大型直线振动筛是洗煤厂的主要设备之一，作为振动机械,不仅工作强度大, 且工作环境十分恶劣，连续工作使其结构极易产生疲劳断裂。

本课题的研究目标是采用对振动筛的结构进行系统动力学健康状况监测与声发射技术离线检测相结合的方式确定结构损伤的部位与损伤的程度。

下横梁是振动筛的主要承载结构件，也是易损伤结构。

本文从振动筛的损伤下横梁识别入手，研究大型机械结构的早期故障诊断方法。

具体研究方法是：
研究近自由状态的下横梁的基本振动特性，提取前两阶弯曲模态振型为研究基础。

系统研究了不同程度损伤所引起这两阶模态频率的相应变化以及频响函数幅值的变化规律，获得了梁的损伤程度与模态频率以及频响函数幅值间变化规律的经验公式；
研究在连接约束状态下的下横梁的基本振动特性，发现在振动筛整体结构中，下横梁的这两阶弯曲模态振型仍然存在，只是模态频率产生向低频方向的移动，且振型略有变异，但弯曲模态的主要特征依然存在。

因此，以此两阶弯曲模态振型为研究基础，研究损伤所引起
这两阶模态频率的相关变化以及频响函数幅值的变化规律，提出了相应的经验公式；
基于对振动筛的子结构一一下横梁及其损伤特征的充分研究，进一步研究下横梁损伤对振动筛的整体振动特性的影响。

发现在特定的频率区间(这里是20Hz-30Hz,含损伤下横的振动筛的频响函数非常明显地向低频方向移动。

对此, 作者做了三项工作:(1)对频响函数在这段频率范围的变化规律做了详细分析;(2) 用小波分析与功率谱分析对时域振动加速度信号进行了处理，提取损伤特征;③ 选取特定的参考点，研究振动信号传输率的变化，提取出损伤特征；
基于对振动筛的振动特性及其子结构损伤的系统研究，提出了振动筛子结构损伤定位方法。

通过含损伤子结构与完好子结构振型的相关分析，可准确确定损伤子结构的位置。

这个方法的提出是本文的创新性土作之一；
研究运行状态下以少测点获取结构损伤信息的建模方法，提出传感器合理布置方法，实现大型结构构件局部损伤的在线检测与定位(确定构件位置)。

这项研究是本文工作的创新点性；
最后，本文用声发射技术对损伤下横梁进行了损伤部位的精确定位，并对裂纹的声发射信号进行了小波分析及功率谱分析，可提取出裂纹的声发射信号特征，为进一步的后续研究探寻路径。

在本文的研究思路上，将设备结构健康状况的在线动力学监测与离线的声发射损伤细节分析技术相结合，提出了大型结构损伤检测的一个新思路。

本文对振动筛结构的健康状况监测、局部结构损伤检测与定位做了基础性研究工作，用多种物理方法提取结构损伤特征，为实际工业应用做了理论准备。