在役钢丝绳缺陷检测方法的研究现状与展望

在役钢丝绳作为提升、运输及承载设备中的关键部件,被广泛应用于矿产、冶金、交通、建筑等国民经济各主要行业和部门。详述了目前应用于在役钢丝绳的各种无损检测方法,比较了他们的优点及不足,提出了采用超声—声发射相结合应用于钢丝绳强度检测的新原理新方法,对于提高在役钢丝绳的检测精度具有较大的理论和应用价值。     

基于图像识别的电梯制动曳引钢丝绳滑移量检测技术

电梯制动系统曳引钢丝绳在制动过程中的滑移量综合反映了电梯的制动能力和曳引能力,滑移量越大,电梯制动能力和曳引能力越弱.钢丝绳的运动图像为钢丝绳纹理周期性变化的图像,适合使用图像识别算法进行处理,使用高帧工业相机,采集电梯制动时钢丝绳运动的视频,提取出钢丝绳所在的区域,结合Hough变换划分割出钢丝绳,再将图像中钢丝绳按...     

钢丝绳无损检测在流动式起重机上的应用

介绍了MTC钢丝绳安全检测仪的检测原理及现场应用,将该仪器用于流动式起重机在役钢丝绳的无损检测,确保了钢丝绳运行的安全和可靠.     

一种新的高速电梯钢丝绳检测装置及检测方法

针对现有的钢丝绳检测方法无法对高速电梯钢丝绳动态特性进行实时检测的问题,提出一种新的高速电梯钢丝绳检测装置及检测方法。分别从检测装置的设计原理、机构设计和检测方法上进行介绍。文中利用新研制的检测装置,通过高速相机对连续采集到的每一张图像进行图像处理,然后运用图像处理技术和算法分析出钢丝绳的振幅、振动相位、振动频率。     

一种钢丝绳芯输送带无损监测装置的应用研究

介绍了一种钢丝绳芯输送带无损监测装置的组成和原理,并从接头图像拼接、接头图像比对和接头图像、输送带等方面对该钢丝绳芯输送带无损监测装置店主要功能进行了介绍。根据实际应用情况可知,ZSX127D钢丝绳芯输送带无损监测装置可以实现在线检测输送带钢丝绳芯,能够自动识别输送带钢丝绳芯断裂、锈蚀或接头伸长等故障,保障矿井主斜井带式输送机安全可靠运行。     

应用数字图像处理技术检测塔式起重机钢丝绳的损伤

介绍1种采用数字图像处理实现塔式起重机钢丝绳损伤检测的新方法,对采集到的钢丝绳图像进行预处理以减少噪声的污染和影响,根据损伤钢丝绳的特征对预处理的图像进行分割,对图像进行数学形态学处理并判断钢丝绳是否存在损伤。     

Gabor变换在轮胎X光图像处理的应用

在轮胎X射线检测图像中,钢丝帘线的不同排列方式构成不同的纹理图像。Gabor函数可提取图像在频域不同尺度和方向的特征,因此采用基于Gabor和Log-Gabor小波对轮胎X射线图像进行特征提取的方法,并用K-means方法进行图像聚类,实现了轮胎不同纹理图像的分割;采用Log-Gabor函数提取不同方向的纹理特征,并对提取结果进行比较,可检测带束层钢丝帘线疏线或顺线缺陷。     

基于小波重构的皮革表面检测方法

纹理表面的瑕疵检测是机器视觉的一个重要研究课题,已经广泛用于各种产品表面质量控制.本文主要研究皮革制造业中皮革纹理表面的检测,并提出了一种有效的选取小波频带重建图像的纹理瑕疵检测方法.该方法首先应用小波基函数在较优的分解级数上对纹理图像进行分解,然后在最佳的分辨率级数上正确的选取平滑图像或者细节图像来重建图像.在重建图像中均匀纹理图案被有效的移除,仅仅保留了局部瑕疵区域,小波频带选取是基于小波系数的能量分布自动确定最佳重构参数.实验表明,该方法有效,可用于实时在线检测.     

基于机器视觉的汽车轮胎磨损检测系统设计CSCD

针对当前采用人工检测方法普遍存在效率低、容易出现误判、检测不方便等问题,提出一种基于机器视觉与图像处理相结合的轮胎纹理深度检测方法,设计了相应的检测系统。通过工业相机和激光器相配合获取汽车轮胎纹理图像,利用图像处理和误差补偿对图像进行处理获取轮胎纹理深度数据。通过实验验证了所提出的方法的可行性,为行驶中检测打下基础。     

基于缺陷理论的起重机械钢丝绳无损检测方法分析

本文首先从起重机械钢丝绳基本结构和特性、缺陷类型和容易损伤的位置阐述了起重机械钢丝绳缺陷理论。接着通过分析钢丝绳人工目测法、磁检测法优缺点,提出利用图像检测技术对起重机械钢丝绳缺陷进行无损检测方法的可能。     

An online defects inspection method for float glass fabrication based on machine vision

Quality control is a crucial issue in a float glass factory, and defects existing in float glass can dramatically depress glass grade. Manual inspection in float glass quality control cannot catch up with the development of float glass industry, and automatic glass defect inspection has been a trend. An online defects inspection method for float glass based on machine vision is presented in this paper, and a distributed online defect inspection system for float glass fabrication is realized. This method inspects defects through detecting the change of image gray levels caused by the difference in optic character between glass and defects. A series of image processing algorithms are set up around the analysis of glass image and the requirements of online inspection system such as reliability, real-time, and veracity. Image filtration based on gradient direction is used to filter noise and reserve the source information of defects. Downward threshold based on adaptive surface removes the background composed with stripes and strengthens defect features. Distortion part and core part of defects are obtained through fixed threshold and OTSU algorithms with gray range restricted, respectively. The fake defects (insects, dust, etc.) are eliminated based on the texture of real defects. The application of an inspection system based on this method in Wuhan glass factory proves this inspection method is effective, accurate, and reliable.     

Automated Surface Inspection Using Gabor Filters

In this paper we present a machine vision system for the automatic inspection of defects in textured surfaces found in industry. The defects to be inspected are those that appear as local anomalies embedded in a homogeneous texture. The proposed method is based on a Gabor filtering scheme that computes the output response of energy from the convolution of a textured image with a specific Gabor filter. The best parameters of a Gabor filter are selected so that the energy of the homogeneous texture is zero, and any unpredictable defeats will generate significantly large energy values. A simple thresholding scheme then follows to discriminate between homogeneous regions and defective regions in the filtered image. This transforms texture differences into detectable filter output. The experiments on structural textures such as leather and sandpaper have shown the effectiveness of the proposed method.     

Surface defect detection in tiling Industries using digital image processing methods: Analysis and evaluation

Ceramic and tile industries should indispensably include a grading stage to quantify the quality of products. Actually, human control systems are often used for grading purposes. An automatic grading system is essential to enhance the quality control and marketing of the products. Since there generally exist six different types of defects originating from various stages of tile manufacturing lines with distinct textures and morphologies, many image processing techniques have been proposed for defect detection. In this paper, a survey has been made on the pattern recognition and image processing algorithms which have been used to detect surface defects. Each method appears to be limited for detecting some subgroup of defects. The detection techniques may be divided into three main groups: statistical pattern recognition, feature vector extraction and texture/image classification. The methods such as wavelet transform, filtering, morphology and contourlet transform are more effective for pre-processing tasks. Others including statistical methods, neural networks and model-based algorithms can be applied to extract the surface defects. Although, statistical methods are often appropriate for identification of large defects such as Spots, but techniques such as wavelet processing provide an acceptable response for detection of small defects such as Pinhole. A thorough survey is made in this paper on the existing algorithms in each subgroup. Also, the evaluation parameters are discussed including supervised and unsupervised parameters. Using various performance parameters, different defect detection algorithms are compared and evaluated.     

Fast Defect Detection in Textured Surfaces Using 1D Gabor Filters

In this paper, we present a fast machine vision method for the automatic inspection of defects in textured surfaces. Traditional 2D Gabor filtering schemes have been shown to be very effective for detecting local anomalies in textured surfaces of industrial materials. However, they are computationally expensive and sensitive to image rotation. In order to alleviate the limitations of 2D Gabor filtering, we first use a 1D ring-projection transformation to compress a 2D grey-level image into a 1D pattern, and then employ a 1D Gabor filter to detect defects embedded in a homogeneous texture. Given a problem with image size N × N and filter window W × W, the computational complexity can be reduced significantly from O(W ² N ² ) in the 2D Gabor space to O(WN ² ) in the 1D Gabor space, and the detection results are invariant to rotation changes of a texture. The experiments on structural textures such as a wooden surface, an LCD display, and a machined surface, and statistical textures such as granite, leather, and sandpaper have shown the efficiency and effectiveness of the proposed method. ID="A1" Correspondence and offprint requests to: Dr Du-Ming Tsai, Department of Industrial Engineering and Management, Yuan-Ze University, 135 Yuan-Tung Road, Nei-Li, Tao-Yuan, Taiwan. E-mail: iedmtsai@saturn.yzu.edu.tw     

基于子区域变尺度高斯拟合的木材表面缺陷识别

为了提高木材的使用效率、避免由于木材缺陷造成生产故障,根据木材缺陷类型对其分类处理是一种有效的手段,但木材缺陷复杂多样且具有诸多相似性使得类别区分成为难点。针对以上问题本文提出了一种基于子区域变尺度高斯拟合模型的缺陷识别方法。首先建立变尺度高斯拟合基本模型,然后将缺陷纹理分成若干子区域,提取各分区的高斯拟合特征并进行融合;将高斯融合特征及圆度和边缘直线度这两个几何特征输入到建立好的BP神经网络模型中进行训练,根据优化训练的网络模型识别缺陷。该方法对自建的SUT-W图库中雪糕棒图像上人工标定的裂缝、矿物线、矿物块和黑节子的准确识别率分别为91.72%、92.77%、92.67%和92.80%,与其他典型纹理检测方法相比,4种缺陷准确识别率最高分别提高9.38%、6.69%、13.55%和10.22%,说明本文方法能够有效地将以上4种缺陷分辨开,具有一定的实际应用价值。     

钢丝微动疲劳裂纹萌生寿命预测研究*

钢丝微动疲劳过程中,钢丝裂纹萌生特性直接影响其裂纹扩展特性,进而制约钢丝微动疲劳寿命,因此开展钢丝微动疲劳裂纹萌生寿命预测研究具有重要意义。基于有限元法、摩擦学理论和断裂力学理论,运用Smith-Watson-Topper（SWT）多轴疲劳寿命准则建立考虑磨损的钢丝微动疲劳裂纹萌生寿命预测模型,基于多种不同的钢丝疲劳参数估算方法对钢丝的微动疲劳裂纹萌生寿命进行了预测,并探究接触载荷、疲劳载荷、交叉角度及钢丝直径等微动疲劳参数对钢丝微动疲劳裂纹萌生寿命的影响规律。结果表明：基于中值法的预测结果最接近实际值;在微动疲劳过程中,钢丝微动疲劳裂纹萌生寿命主要与接触载荷和疲劳载荷相关。通过引入微动损伤参数建立简化的适用于钢丝绳的钢丝微动疲劳裂纹萌生寿命预测模型,通过与考虑磨损的预测模型计算结果进行对比验证了该模型的准确性。     

Defect inspection system for steel wire rods produced by hot rolling process

A vision-based inspection system has been investigated in order to improve the quality of products and processes found in various industries. In this paper, we propose a new defect detection algorithm for steel wire rods produced by the hot rolling process. Because the steel wire rods are long cylinder rods with a circular cross section, the brightness at the sides and center is inconsistent. Moreover, the various types of steel wire rods and the presence of scales affect the reflection properties of the rod surface. In order to resolve the abovementioned difficulties, the use of dynamic programming and a discrete wavelet transform are proposed. An adaptive local binarization method is used to further reduce the effects of scale. The effectiveness of the proposed method is shown by means of experiments conducted on images of steel wire rods that were obtained from an actual steel production line.     

一种基于图像纹理特征的钢球振动值检测新方法

利用回归分析法,建立了基于图像纹理特征的钢球振动值预测模型,提出了一种检测钢球振动值的新方法。     

Automatic defect inspection for LCDs using singular value decomposition

Thin film transistor liquid crystal displays (TFT-LCDs) have become increasingly popular and dominant as display devices. Surface defects on TFT panels not only cause visual failure, but result in electrical failure and loss of LCD operational functionally. In this paper, we propose a global approach for automatic visual inspection of micro defects on TFT panel surfaces. Since the geometrical structure of a TFT panel surface involves repetitive horizontal and vertical elements, it can be classified as a structural texture in the image. The proposed method does not rely on local features of textures. It is based on a global image reconstruction scheme using the singular value decomposition (SVD). Taking the image as a matrix of pixels, the singular values on the decomposed diagonal matrix represent different degrees of detail in the textured image. By selecting the proper singular values that represent the background texture of the surface and reconstructing the matrix without the selected singular values, we can eliminate periodical, repetitive patterns of the textured image, and preserve the anomalies in the restored image. In the experiments, we have evaluated a variety of micro defects including pinholes, scratches, particles and fingerprints on TFT panel surfaces, and the result reveals that the proposed method is effective for LCD defect inspections.