Evaluation of roof cutting by directionally single cracking technique in automatic roadway formation for thick coal seam mining

Automatic roadway formation by roof cutting is a sustainable nonpillar mining method that has the potential to increase coal recovery,reduce roadway excavation and improve mining safety.In this method,roof cutting is the key process for stress relief,which significantly affects the stability of the formed roadway.This paper presents a directionally single cracking(DSC)technique for roof cutting with considerations of rock properties.The mechanism of the DSC technique was investi-gated by explicit finite element analyses.The DSC technique and roof cutting parameters were evaluated by discrete element simulation and field experiment.On this basis,the optimized DSC technique was tested in the field.The results indicate that the DSC technique could effectively control the blast-induced stress distribution and crack propagation in the roof rock,thus,achieve directionally single cracking on the roadway roof.The DsC technique for roof cutting with optimized parameters could effectively reduce the deformation and improve the stability of the formed roadway.Field engineering application verified the feasibility and effectiveness of the evaluated DSC technique for roof cutting.

An automatic loading system for rock core testing with an industrial CT scanner

A new type of a loading and measuring system was developed for testing failure and deformation of rock core samples with an industrial CT （ICT） scanner.The loading and measuring system consisted of a loading system and a computer control system.The maximum servo-controlled force was 2 tonnes.The new system was a high-stiffness system with a small size.During ICT tests,rock core samples could be easily loaded in the axial direction.So the initiation,propagation,and coalescence of cracks in core samples were observed on ICT images.

Pavement Cracks Coupled With Shadows:A New Shadow-Crack Dataset and A Shadow-Removal-Oriented Crack Detection Approach

Automatic pavement crack detection is a critical task for maintaining the pavement stability and driving safety.The task is challenging because the shadows on the pavement may have similar intensity with the crack,which interfere with the crack detection performance.Till to the present,there still lacks efficient algorithm models and training datasets to deal with the interference brought by the shadows.To fill in the gap,we made several contributions as follows.First,we proposed a new pavement shadow and crack dataset,which contains a variety of shadow and pavement pixel size combinations.It also covers all common cracks(linear cracks and network cracks),placing higher demands on crack detection methods.Second,we designed a two-step shadow-removal-oriented crack detection approach:SROCD,which improves the performance of the algorithm by first removing the shadow and then detecting it.In addition to shadows,the method can cope with other noise disturbances.Third,we explored the mechanism of how shadows affect crack detection.Based on this mechanism,we propose a data augmentation method based on the difference in brightness values,which can adapt to brightness changes caused by seasonal and weather changes.Finally,we introduced a residual feature augmentation algorithm to detect small cracks that can predict sudden disasters,and the algorithm improves the performance of the model overall.We compare our method with the state-of-the-art methods on existing pavement crack datasets and the shadow-crack dataset,and the experimental results demonstrate the superiority of our method.

基于改进YOLOv5s算法的桥梁裂缝自动检测及分类

为了克服传统人工裂缝检测方法费时费力、局限性大等弊端与不足,提出一种基于深度学习的裂缝自动检测与分类方法。采用YOLOv5s算法作为基础,引入2种不同的注意力机制——SENet和Coordinate Attention,这些机制从大量数据中快速筛选出高价值信息,从而提高了YOLOv5s模型在裂缝识别和分类方面的效率。原始的YOLOv5s模型在1500张包含4种类型裂缝的图像上的检测结果为89.2%,引入注意力机制后,模型精度分别提高了5.7%和7.1%,达到了94.9%和96.3%。结果表明,改进后的YOLOv5s算法可以实现桥梁裂缝的自动检测及分类,在实际的桥梁性能测试中具有广泛应用前景。

长输管道自动焊对接环焊缝熔铜裂纹的检测与判定

熔铜裂纹是长输管道自动焊对接环焊缝常见缺陷之一,PAUT(相控阵超声)检测、TOFD(超声衍射时差法)检测、AUT(全自动超声)检测是发现熔铜裂纹的有效检测方式,射线检测对熔铜裂纹的检测具有较大的局限性,可能表现出非典型裂纹特征,甚至无法发现熔铜裂纹。使用各检测方法对熔铜裂纹进行检测试验,结果表明,对于自动焊熔铜裂纹的判定,需结合以上多种检测方法进行准确定性。

交流电磁场钢轨表面裂纹高速检测信号处理模块开发

交流电磁场检测(Alternating Current Field Measuremnt,ACFM)技术因受速度和提离效应影响小,在钢轨裂纹高速检测中具有良好的应用前景,但信号拾取速度会导致裂纹信号频率发生偏移,造成量化误差、漏检和误检。为提高ACFM技术在钢轨裂纹检测中的可靠性,基于现场可编程门阵列(Field Programmable Gate Array,FPGA)研发了交流电磁场钢轨表面裂纹高速检测信号处理模块,采用理论推导方法分析了检测速度对裂纹信号频率偏移的影响,构建了检测速度与裂纹信号有效频率范围之间的关系。采用FPGA开发了具有截止频率自动跟踪检测速度的数字正交锁相放大器,同时,开发了完整的钢轨表面裂纹检测装置和实验系统,最终,通过实验验证了模块的检测能力和稳定性。

催化裂解制乙烯装置三机组全自动控制的应用

电机-主风机-烟机三机组是催化裂解(DCC)制乙烯装置中的核心设备,直接影响工艺装置的稳定运行和能耗,且操作难度高。介绍了主风机的工艺控制需求,通过分析主风机、烟机的运行特点,增加了主风机性能控制及烟机自动暖机、升速、并网控制,成功实现了主风机自动控制负荷、烟机一键启动,达到了预期的效果。相比手动操作,该功能更加高效、安全、可靠和人性化,为装置高效平稳运行提供有力保障。

催化裂化分馏系统的自控设计及仪表选型研究

按照“化工装置自控工程设计规定”根据工艺提出的检测及控制要求,对催化裂化分馏装置的分馏塔、汽提塔、油气分离器、原料油缓冲罐和回炼油管的液位、温度、压力等进行控制和检测,并对流程做了简单的介绍。根据催化裂化分馏装置的特点选择了合适的仪表和调节阀。

石油催化裂化装置工艺自控系统设计和仪表选型分析研究

随着国民经济和社会科技的发展,人们对石油化工产品的需求不断增多,促进了石油化工行业的快速发展.在石油化工行业中,石油催化裂化装置在行业中承担着"承上启下"的重要作用。按照石油化工装置自控工程设计规定以及相关生产工艺的控制和检测要求,对石油催化裂化装置的分馏系统、吸收稳定系统的温度、压力等进行控制和检测,并根据相关设计规范及石油催化裂化装置的生产特点选择出合适的仪表和控制阀。

石油催化裂化分馏系统的自控化设计及仪表选型分析

按照“化工装置自控工程设计规定”,根据工艺提出的检测及控制要求,对催化裂化分馏装置的分馏塔、反应-再生系统、回炼油罐、汽提塔的液位、温度、压力等进行控制和检测,并对流程做了简单的介绍。根据催化裂化分馏装置的特点选择了合适的仪表和DCS系统。

L555M管线钢自动焊工艺焊接裂纹原因分析

全自动焊技术因其可靠的焊接质量、高效的焊接效率在油气长输管道建设中被广泛使用。针对国内某在建天然气管道工程L555M钢在焊接过程中出现的裂纹,采用化学成分分析、无损检测、宏观分析、微观分析、扫描电镜联合能谱等分析方法,分析了该裂纹产生原因。研究结果表明:该裂纹缺陷为铜元素富集所致。铜元素富集的原因为:全自动焊接设备的铜质导电嘴在焊接过程中与焊缝坡口碰壁产生短路,短路电流产生的高温导致铜质导电嘴部分熔化,随着焊接完成,被熔化的铜元素聚集在母材和焊缝的晶界处,晶界铜富集区域在焊缝残余拉应力作用下发生脆化进而导致产生裂纹。

Intelligent Segmentation and Measurement Model for Asphalt Road Cracks Based on Modified Mask R-CNN Algorithm

Nowadays, asphalt road has dominated highways around the world. Among various defects of asphalt road, crackshave been paid more attention, since cracks often cause major engineering and personnel safety incidents. Currentmanual crack inspection methods are time-consuming and labor-intensive, and most segmentation methods cannot detect cracks at the pixel level. This paper proposes an intelligent segmentation and measurement model basedon the modified Mask R-CNN algorithm to automatically and accurately detect asphalt road cracks. The modelproposed in this paper mainly includes a convolutional neural network (CNN), an optimized region proposalnetwork (RPN), a region of interest (RoI) Align layer, a candidate area classification network and a Mask branch offully convolutional network (FCN). The ratio and size of anchors in the RPN are adjusted to improve the accuracyand efficiency of segmentation. Soft non-maximum suppression (Soft-NMS) algorithm is developed to improvethe segmentation accuracy. A dataset including 8,689 images (512× 512 pixels) of asphalt cracks is established andthe road crack is manually marked. Transfer learning is used to initialize the model parameters in the trainingprocess. To optimize the model training parameters, multiple comparison experiments are performed, and the testresults show that the mean average precision (mAP) value and F1-score of the optimal trained model are 0.952 and0.949. Subsequently, the robustness verification test and comparative test of the trained model are conducted andthe topological features of the crack are extracted. Then, the damage area, length and average width of the crackare measured automatically and accurately at pixel level. More importantly, this paper develops an automatic crackdetection platform for asphalt roads to automatically extract the number, area, length and average width of cracks,which can significantly improve the crack detection efficiency for the road maintenance industry.

Image Preprocessing Methods to Identify Micro-cracks of Road Pavement

Standards of highway conservation and maintenance are improved gradually following the improvement of requirements of road service. Before obvious damage such as obvious cracking (block,transverse, longitudinal ) and rutting emerge, inconspicuous distress (micro-cracks, polishing, pockmarked) is generated previously. These inconspicuous distresses may provide basis and criteria for pavement preventive maintenance. Currently most of preventive conservation measures are determined by experienced experts in maintenance and repair of road after site visits. Thus method is difficult in operation, and has a certain amount of instability as it is based on experience and personal knowledge. In this paper, camera and laser were used for automated high-speed acquisition images. Methods to preprocess pavement image are compared. The pretreatment method suitable for analyze micro-cracks picture is elected, an effective way to remove shadow is also proposed.

基于生成对抗网络的隧道裂缝自动分割算法研究

传统裂缝识别技术需要大量带标签的裂缝图像作为试验数据集,为减少裂缝图像标注的工作量,利用生成对抗网络图像分割的特性与优势,构建一种Crack-GAN网络用于隧道裂缝自动分割。Crack-GAN网络结构集成2个模块:融合残差化U-Net网络的生成器网络和利用全卷积网络生成置信图的判别器网络。首先U-Net模块使用密集的残差模块来生成保留细粒度信息的深层表示,然后判别器来判断输入真假,并以端到端的方式训练,再经过生成对抗模型之间不断迭代,使生成模型达到分割裂缝的最优状态。试验表明,Crack-GAN网络在自制铁路隧道裂缝数据集上的像素准确性为98.35%,精准率为71.23%,召回率为80.78%,F 1得分为75.98%,单次检测时间80 ms,综合表现优于U-Net和CrackSegNet。

乙烯装置急冷系统管道腐蚀和急冷水乳化问题分析及处理措施

针对乙烯裂解装置在工业化应用过程中出现的急冷系统管道腐蚀和急冷水乳化的问题,通过技术改造,增加新的缓蚀剂注入点,更换缓蚀剂类型及引入pH值自动控制系统等,有效解决急冷系统管道腐蚀和急冷水乳化的问题,确保了乙烯装置“安稳长满优”运行。

基于机器视觉的硅锭端面隐裂检测方法

硅锭在生产过程中难以避免有端面隐裂的发生,严重影响生产线的安全性能和生产效率,为此提出一种改进背景差分的硅锭端面隐裂自动检测方法;首先,分析端面隐裂的特点,采用中值滤波来对图像进行预处理;其次,采用图片样本库训练得到背景图像,并对常用差分算法进行改进,在对图像进行差分的同时增强隐裂缺陷;再根据隐裂缺陷的最大比例不超过10%,进一步去除差分图片中的大部分背景点;最后,再提取隐裂缺陷区域,对区域进行连通域分析,并根据缺陷区域面积来判断端面是否有隐裂;对硅碇端面隐裂缺陷的仿真结果和现场实践表明,该方法识别的准确率达96.4%,召回率达98.5%,识别时间低于200 ms,很好地满足了现场运行的需求。

基于改进YOLOv5的道路病害智能检测

针对现有道路表观病害检测识别精度低、漏判、误检率高的问题,提出了一种改进的道路表观病害检测高精度识别模型(improved pavement detection-YOLOv5,IPD-YOLOv5)。在YOLOv5的主干特征提取网络中添加由不同空洞卷积组成的ASPP模块,引入SE-Net注意力机制以加强算法从裂缝图像中提取不同尺度特征的能力,实现多尺度特征图的有效融合。结果表明:较传统检测算法,所提的IPD-YOLOv5模型在道路裂缝病害检测上的识别精度最高,其中平均精度比未改进的YOLOv5算法提高了7.47%,漏判率降低了10.29%。

乙烯裂解炉自动烧焦控制系统开发及应用

以乙烯裂解炉自动烧焦为研究对象,应用顺序控制技术和先进过程控制技术开发了乙烯裂解炉自动烧焦系统,该系统可按照预设的步骤及控制参数完成裂解炉烧焦过程。该系统应用先进过程控制的模型预测技术,实现了炉管出口温度(COT)的稳定及卡上限控制,自动烧焦过程中的A/B炉膛COT温度比手动烧焦分别提高2.0℃和0.9℃,提升了烧焦效率;自动烧焦过程中的烧焦量平稳增加,有效避免了超温情况,降低了炉管损伤的风险,每次烧焦时间平均节约1.4 h。

桥梁裂缝病害自动识别算法研究

针对辽宁省内公路桥梁病害现状,基于混凝土桥梁上病害主要是裂缝的状况,利用双线性插值法对采集的桥梁图像进行缩小,再用直方图均衡化的方法加强图像,然后用中值滤波对图像进行降噪处理,利用相干增强滤波器和区域生长法处理数据,保持病害区域的完整,最后提取连通域,计算形状特征完成对桥梁裂缝病害的识别。

汽车自动调整臂壳体失效原因分析

进行台架试验时,若干件自动调整臂壳体发生开裂现象。为了分析壳体开裂原因,实验室随机抽取了3件带裂纹壳体零件进行了宏观观察、断口分析、金相检查、硬度检查、化学分析等。结果发现:失效零件工作环境存在周期性循环载荷作用,致使在调整臂壳体壁厚较薄处优先萌生疲劳裂纹,并扩展至零件彻底失效。检查发现,在裂纹源区外壁存在严重磨损,磨损不仅破坏了材料表面完整性,还使壁厚进一步减薄,降低了零件的抗疲劳强度。通过优化系统配合度,减小磨损,提高自身抗疲劳强度及表面耐磨性,可以有效控制此类故障的发生。