Article(id=1187027204639699444, tenantId=1146029695717560320, journalId=1185652524569653253, issueId=1187027204178326004, articleNumber=1009-5438(2024)06-0001-03, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1717171200000, receivedDateStr=2024-06-01, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1760938939485, onlineDateStr=2025-10-20, pubDate=1735056000000, pubDateStr=2024-12-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1760938939485, onlineIssueDateStr=2025-10-20, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1760938939485, creator=13701087609, updateTime=1760938939485, updator=13701087609, issue=Issue{id=1187027204178326004, tenantId=1146029695717560320, journalId=1185652524569653253, year='2024', volume='50', issue='6', pageStart='1', pageEnd='98', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1760938939375, creator=13701087609, updateTime=1760939191531, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1187028261868548309, tenantId=1146029695717560320, journalId=1185652524569653253, issueId=1187027204178326004, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1187028261868548310, tenantId=1146029695717560320, journalId=1185652524569653253, issueId=1187027204178326004, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=1, endPage=3, ext={EN=ArticleExt(id=1187100783167291762, articleId=1187027204639699444, tenantId=1146029695717560320, journalId=1185652524569653253, language=EN, title=Intelligent Detection Technology of Industrial Buildings (Structures), columnId=1187100783049851249, journalTitle=Science & Technology of Baotou Steel, columnName=Expert Forum, runingTitle=null, highlight=null, articleAbstract=

With the development of digital and intelligent technologies in each field, they are widely applied due to the high efficiency and accuracy they brought. The digital and intelligent technologies for detection and routing inspection of industrial buildings (structures) are increasingly developed. In this paper, the important roles of intelligent detection technologies in safe operations of structure for industrial buildings (structures) are compared and explained through the specific analysis on applications of such three common intelligent detection technologies in detection of industrial buildings (structures) as unmanned aerial vehicle platform technology, digital image recognition technology and network technology of intelligent sensor for Internet of things.

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随着数字化、智能化技术在各个领域的发展,带来的高效性、准确性使其在各个领域得到广泛的应用,工业建(构)筑物检测和巡检的智能化、数字化技术也日益发展。文章从无人机平台技术、数字化图像识别技术、物联网智能传感器网络技术三项常见智能检测技术在工业建(构)筑物检测中的应用进行具体分析,对工业建(构)筑物结构安全运行起到的重要作用进行对比说明。

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王 亮(1968-),男,河北省献县人,正高级工程师,一级注册结构工程师,现从事工程项目管理、结构设计、结构鉴定等工作。

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王 亮(1968-),男,河北省献县人,正高级工程师,一级注册结构工程师,现从事工程项目管理、结构设计、结构鉴定等工作。

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王 亮(1968-),男,河北省献县人,正高级工程师,一级注册结构工程师,现从事工程项目管理、结构设计、结构鉴定等工作。

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工业建(构)筑物智能检测技术
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王亮 1 , 孙广宇 2 , 李朝辉 1 , 袁朋举 2 , 徐长彬 2 , 国秀英 2
包钢科技 | 专家论坛 2024,50(6): 1-3
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包钢科技 | 专家论坛 2024, 50(6): 1-3
工业建(构)筑物智能检测技术
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王亮1, 孙广宇2, 李朝辉1, 袁朋举2, 徐长彬2, 国秀英2
作者信息
  • 1.内蒙古包钢西创集团有限责任公司生产建设部,内蒙古 包头 014010
  • 2.包头冶金建筑研究院,内蒙古 包头 014010
  • 王 亮(1968-),男,河北省献县人,正高级工程师,一级注册结构工程师,现从事工程项目管理、结构设计、结构鉴定等工作。

Intelligent Detection Technology of Industrial Buildings (Structures)
Wang Liang1, Sun Guang-yu2, Li Zhao-hui1, Yuan Peng-ju2, Xu Chang-bin2, Guo Xiu-ying2
Affiliations
  • 1. Production and Construction Dept. of Inner Mongolia Baotou Steel Northwest Pioneer Group Co., Ltd., Baotou 014010, Inner Mongolia Autonomous Region, China
  • 2. Baotou Metallurgical Construction Institute, Baotou 014010, Inner Mongolia Autonomous Region, China
出版时间: 2024-12-25
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随着数字化、智能化技术在各个领域的发展,带来的高效性、准确性使其在各个领域得到广泛的应用,工业建(构)筑物检测和巡检的智能化、数字化技术也日益发展。文章从无人机平台技术、数字化图像识别技术、物联网智能传感器网络技术三项常见智能检测技术在工业建(构)筑物检测中的应用进行具体分析,对工业建(构)筑物结构安全运行起到的重要作用进行对比说明。

工业建(构)筑物  /  智能化  /  数字化

With the development of digital and intelligent technologies in each field, they are widely applied due to the high efficiency and accuracy they brought. The digital and intelligent technologies for detection and routing inspection of industrial buildings (structures) are increasingly developed. In this paper, the important roles of intelligent detection technologies in safe operations of structure for industrial buildings (structures) are compared and explained through the specific analysis on applications of such three common intelligent detection technologies in detection of industrial buildings (structures) as unmanned aerial vehicle platform technology, digital image recognition technology and network technology of intelligent sensor for Internet of things.

industrial buildings (structures)  /  intelligent  /  digital
王亮, 孙广宇, 李朝辉, 袁朋举, 徐长彬, 国秀英. 工业建(构)筑物智能检测技术. 包钢科技, 2024 , 50 (6) : 1 -3 .
Wang Liang, Sun Guang-yu, Li Zhao-hui, Yuan Peng-ju, Xu Chang-bin, Guo Xiu-ying. Intelligent Detection Technology of Industrial Buildings (Structures)[J]. Science & Technology of Baotou Steel, 2024 , 50 (6) : 1 -3 .
工业建(构)筑物的结构安全对工业生产以及保证人民生命财产安全起着至关重要的作用。对其结构安全性的定期检测、巡检及监测是管理过程中必不可少的环节。然而传统的检测技术总是存在一些局限性,人工检测效率、数据处理、监测范围等都存在困难,甚至一些检测项难以实现。随着人工智能、大数据、物联网等先进技术的出现,工业建(构)筑物智能化、数字化检测及监测大大提高了检测效率及准确性,对工业建(构)筑物结构安全运行起到重要作用。
传统的工业建(构)筑物结构检测都是由人工完成,配备一些便携式手持仪器,需要检测和巡检人员手动检测或目测,效率较低。人工检测往往是抽检,不能做到面面俱到。在工业生产环境下,一些区域人员无法进入,除此之外,其他生产区域也存在一定危险性。传统的结构检测比较依赖专业人员的主观判断,往往需要丰富的经验,这就形成了局限性,有经验的人员不足,不能准确判断;传统的结构检测往往是周期性的,无法做到实时监测,对一些突发事件不能及时预警。
随着科技的不断发展,无人机技术的应用范围越来越广泛。无人机作为一种高效、灵活的工具,被广泛用于各个领域,基于无人机平台的检测技术具有高效、高精度、低风险、低成本的特点。对于工业高耸结构的人工检测、巡检,往往采用目测、望远镜观察或者大型登高设施登高检测等,目测及望远镜精度有限,而登高作业难度极大,危险性极高,这时无人机的优势就显而易见。
无人机可覆盖的区域较广,可在复杂且人员难以进入的环境中进行检测,快速、灵活地从空中进行数据及图像采集。基于无人机平台的多种检测系统应运而生,无人机可以搭载各种传感器,如高清相机、红外热成像仪、激光雷达(LiDAR)、多光谱传感器等,用于不同类型结构的多维度检测[1-2]
近年来,人工智能技术的发展在各个领域取得了显著成效,数字化图像识别技术在大数据人工智能发展的同时变得更加成熟。利用计算机对数字图像中的特征、模式进行分析和提取,将提取的特征与预先训练好的模型进行比较和分类,从而识别图像中的对象或场景,免去人工对图像的分析与识别,大大提高了工作效率。
传感器技术与物联网结合在工业结构监测中的应用日益广泛,智能传感器网络对结构状态实时监测,通过数据分析做出预警判断。
智能传感器网络涵盖各种类型的传感器,如应变计、加速度计、温度传感器、压力传感器等,能够实时监测结构的振动、应力、温度、湿度等多个参数,从而全面了解结构的工作状态和健康状况。传感器网络通过实时数据采集和传输,将结构的运行数据实时反馈到监测系统中,数据可以通过物联网传输到云端或中心服务器,进行实时分析和处理,以提供及时的结构健康评估和预警信息。
利用智能传感器网络可以有效降低结构监测和维护的成本,减少人工检测的频率和需要,同时降低人员进入危险区域的风险,提高工作安全性和效率,有效预防结构损伤和提高结构的使用寿命,是工业生产安全管理和维护的重要技术手段之一。
基于无人机检测平台搭载红外热成像仪对烟囱筒壁及内衬隔热层缺陷进行检测。在传统的结构检测中,需在停产、高空、密闭空间等情况下进行目测,这种方式工作效率低且主观性大,对于高空复杂的地方,检测工作极其危险且不方便。随着科技的发展,无人机在结构检测中的普及,以及非接触式无损检测技术的快速发展,可以通过无人机平台红外热成像技术对结构进行智能、精准检测及评估,使传统的检测方式发生重大转变,不仅仅降低了作业风险,而且使得检测向高效、精准的方向发展。现场采用红外热成像仪对烟囱内衬腐蚀状况进行检测,烟囱内衬若腐蚀严重,将发生剥落或变形,影响其隔热效果,隔热薄弱区会产生大量的热泄漏,并在烟囱外壁形成过热辐射区,烟囱外壁会出现温度差异。由于物体局部温差的存在,必然导致红外辐射强度不同,利用红外热像仪即可检测出温度的变化状况,进而判断缺陷的情况。
基于无人机建筑结构缺陷检测装置,通过设置标记结构,达到高效检测的目的。首先在储料箱内部注入红色液体,之后利用无人机的高清摄像头对烟囱高处立面全面检测。当检测到缺陷时,驱动水泵可将储料箱内部的红色液体沿导向管打入喷头内,并沿喷头喷涂至缺陷位置,鲜艳的红色可方便检测及加固施工人员快速准确地找到缺陷位置,大大提高了工作效率。图1为烟囱红外热成像图。
传感器网络负责数据采集和现场存储,数据网关与传输网络提供数据的传输通道,应用层的云服务器提供后台的数据库支持及服务器支持,应用层的展示部分负责按照设定的模型进行数据分析及展示。
在脱硫烟道塔架摆动监测中采用基于双轴倾角传感器的高耸结构摆动云监测技术。由于塔架高度达到50 m,受大风影响会出现摆动现象,为保证生产安全,对其进行摆动监测。采用常规的全站仪、精密水准仪等设备对塔架进行监测受人为影响大,若进行全天候监测,需投入的人工成本高,操作难度大,监测结果易受人为影响。基于双轴倾角传感器的高耸结构摆动云检测从实际出发,与日常工作结合,从经济性、可操作性、准确性等综合考虑,相比传统手段更加方便、快捷、灵活。监测采用无线双轴倾角传感器,其精度高、体积小,防水、防尘,便于安装,加入自动供电系统同时配合物联网云平台技术达到对结构的实时监测,保证检测设备准确地采集建筑物数据,便于排除环境影响,对于频域周期内结构整体观测具有极强的现实意义。
基于大容量光栅阵列传感光纤的传感物联网可以全时、全域监测地铁、隧道结构的振动模态与动态分布、隧道温度分布和火灾监测;机场跑道布设光栅阵列传感网络,实时采集、储存并分析判断振动、温度、应变、湿度、水膜、地下水位等数据,形成可视化的大数据云平台。图2为智能传感器网络结构图。
智能检测技术的应用为工业生产提供更多的便利,同时也带来新的挑战。结合人工智能和专家系统,建筑结构智能检测技术能够提供智能化的维护决策支持,帮助工程师和管理人员制定最佳维护方案。建筑结构智能检测技术将在未来为建筑结构的安全性、可持续性和维护效率带来重大提升,成为建筑工程领域重要的技术创新方向之一。
发展的同时也势必带来新的挑战,加大对工业建筑智能检测技术的研发投入,推动相关技术的创新和突破,同时应建立健全工业建筑智能检测技术的标准体系,规范技术应用和市场秩序。
参考文献 引证文献
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[1]
关贤军, 郭玲, 单伽锃, 等. 基于无人机成像的烟囱表面缺陷检测技术研究[J]. 北京建筑大学学报, 2022(3):77-83.
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魏思航, 刘宇飞, 刘家豪. 基于无人机与数字图像法的混凝土结构表面裂缝检测应用研究[J]. 特种结构, 2020(5):107-111.
2024年第50卷第6期
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  • 接收时间:2024-06-01
  • 首发时间:2025-10-20
  • 出版时间:2024-12-25
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    1.内蒙古包钢西创集团有限责任公司生产建设部,内蒙古 包头 014010
    2.包头冶金建筑研究院,内蒙古 包头 014010
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2种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
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Genus
种数
Number of
species
占总种数比例
Percentage of total
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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