Article(id=1188049577488822869, tenantId=1146029695717560320, journalId=1185652524569653253, issueId=1188049574255018819, articleNumber=1009-5438(2024)01-0075-04, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1701532800000, receivedDateStr=2023-12-03, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1761182692167, onlineDateStr=2025-10-23, pubDate=1708790400000, pubDateStr=2024-02-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1761182692167, onlineIssueDateStr=2025-10-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1761182692167, creator=13701087609, updateTime=1761182692167, updator=13701087609, issue=Issue{id=1188049574255018819, tenantId=1146029695717560320, journalId=1185652524569653253, year='2024', volume='50', issue='1', 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=1761182691396, creator=13701087609, updateTime=1761290996781, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1188503840023265810, tenantId=1146029695717560320, journalId=1185652524569653253, issueId=1188049574255018819, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1188503840023265811, tenantId=1146029695717560320, journalId=1185652524569653253, issueId=1188049574255018819, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=75, endPage=78, ext={EN=ArticleExt(id=1188049577685955161, articleId=1188049577488822869, tenantId=1146029695717560320, journalId=1185652524569653253, language=EN, title=Analysis and Detection of Operation Failures for Conveyor Belt of Bulk Materials, columnId=1187100786392711543, journalTitle=Science & Technology of Baotou Steel, columnName=Equipment and Automation, runingTitle=null, highlight=null, articleAbstract=

There are often such phenomena as spillage, firing, scratch of foreign objects and blockage as well as such failures as off tracking, slipping, scratch and tearing for conveyor belt of iron and steel enterprises. Moreover, the causes of various failures are analyzed, advantages and disadvantages of using various switches for belt failure detection are compared, a method of detecting various failures with images is proposed as well as adaptability of 2D and 3D image detection methods are compared. At last, the architecture of establishing intelligent belt detection platform for iron and steel enterprises is briefly described.

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钢铁企业输送皮带经常发生撒料、着火、异物划伤、堵料等现象,以及输送皮带发生跑偏、打滑、划伤、撕裂等故障。分析了产生各类故障的原因,对比了目前采用各类开关进行皮带故障检测的优缺点,提出了采用图像检测各类故障的方法,并比较了2D与3D图像检测方法的适应性。文章最后简述了钢铁企业构建智能皮带检测平台的架构。

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芦建文(1969-),男,山西省朔州市人,硕士,正高级工程师,现从事计算机与自动化应用研究工作。

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芦建文(1969-),男,山西省朔州市人,硕士,正高级工程师,现从事计算机与自动化应用研究工作。

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芦建文(1969-),男,山西省朔州市人,硕士,正高级工程师,现从事计算机与自动化应用研究工作。

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散装物料输送皮带运行故障分析与检测
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芦建文 1 , 范云鹏 2 , 徐斌 2 , 赵楠 1
包钢科技 | 设备与自动化 2024,50(1): 75-78
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包钢科技 | 设备与自动化 2024, 50(1): 75-78
散装物料输送皮带运行故障分析与检测
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芦建文1, 范云鹏2, 徐斌2, 赵楠1
作者信息
  • 1 内蒙古新联信息产业公司,内蒙古 包头 014010
  • 2 内蒙古包钢钢联股份有限公司仓储中心,内蒙古 包头 014010
  • 芦建文(1969-),男,山西省朔州市人,硕士,正高级工程师,现从事计算机与自动化应用研究工作。

Analysis and Detection of Operation Failures for Conveyor Belt of Bulk Materials
Jian-wen Lu1, Yun-peng Fan2, Bin Xu2, Nan Zhao1
Affiliations
  • 1 Inner Mongolia Xinlian Information Industry Co., Ltd., Baotou 014010, Inner Mongolia Autonomous Region, China
  • 2 Storage Center of Inner Mongolia Baotou Steel Union Co., Ltd., Baotou 014010, Inner Mongolia Autonomous Region, China
出版时间: 2024-02-25
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钢铁企业输送皮带经常发生撒料、着火、异物划伤、堵料等现象,以及输送皮带发生跑偏、打滑、划伤、撕裂等故障。分析了产生各类故障的原因,对比了目前采用各类开关进行皮带故障检测的优缺点,提出了采用图像检测各类故障的方法,并比较了2D与3D图像检测方法的适应性。文章最后简述了钢铁企业构建智能皮带检测平台的架构。

输送皮带  /  故障分析  /  智能诊断

There are often such phenomena as spillage, firing, scratch of foreign objects and blockage as well as such failures as off tracking, slipping, scratch and tearing for conveyor belt of iron and steel enterprises. Moreover, the causes of various failures are analyzed, advantages and disadvantages of using various switches for belt failure detection are compared, a method of detecting various failures with images is proposed as well as adaptability of 2D and 3D image detection methods are compared. At last, the architecture of establishing intelligent belt detection platform for iron and steel enterprises is briefly described.

conveyor belt  /  failure analysis  /  intelligent diagnosis
芦建文, 范云鹏, 徐斌, 赵楠. 散装物料输送皮带运行故障分析与检测. 包钢科技, 2024 , 50 (1) : 75 -78 .
Jian-wen Lu, Yun-peng Fan, Bin Xu, Nan Zhao. Analysis and Detection of Operation Failures for Conveyor Belt of Bulk Materials[J]. Science & Technology of Baotou Steel, 2024 , 50 (1) : 75 -78 .
包钢(集团)公司是一家年钢铁产量超1 500万t的特大型钢铁联合企业。在钢铁生产过程中,有数千万吨的铁矿石、铁矿粉、原煤、洗精煤、焦炭等物料需要在厂内各工序、料场间倒运。目前以汽车运输、厂内铁路运输以及皮带输送为工序间物料主要倒运方式,其中,皮带输送是成本最低、最经济的方式。包钢(集团)公司目前有近千条输送皮带承担厂内散装物料倒运任务。由于常年输送大量铁矿粉等散装物料,皮带机设备的作业环境具有粉尘大、环境温度温差大、设备运行作业量大、设备维护困难等特点,造成皮带经常出现跑偏、堵料、撕裂、打滑、划伤、燃烧、撒料等缺陷和故障,以上这些设备故障与物料事故会造成设备损坏,停止物料输送进程,影响整条生产线生产,造成经济损失。
散装物料输送皮带常见故障有三类:输送皮带撕裂等问题引起的故障,皮带托辊等传动机构摩擦损坏引起的故障以及输送物料燃烧等问题引起的故障。皮带由于受到尖利的物体划伤、皮带磨损或者皮带连接处断开等都会造成皮带受伤损坏;皮带托辊磨损、皮带安装不合适或者设备长时间运行等都可能造成皮带跑偏、打滑等设备故障;输送的散装物料有洗精煤、原煤等各种易燃物料,经常出现物料自燃的情况,造成皮带及设备烧损。以上问题都会造成输送皮带停运以及输送物料的损失,造成生产线停产。
皮带输送过程中发生的跑偏、钢丝抽芯、异物划伤等原因会造成皮带撕裂与划伤。输送带传送过程中,如果输送带长期跑偏向一侧,由于输送带纵向拉力不均匀,容易产生跑偏撕裂。大重量物料长期在下料口的落料冲击会砸断输送带中的钢丝,尖锐的钢丝断头会从输送带表面穿出,很容易被托辊、滚筒等传动机构卷入,当输送带不断向前运转,卷入托辊的钢丝将不断的从输送带抽出,造成皮带撕裂拉伤;另外清扫刮板也容易挂住钢丝断头,撕裂拉伤输送带。
异物划伤是输送皮带的常见故障。散装大宗物料里面会有大块物料、长杆状尖锐铁器等各种异物。在北方的冬季,含水的洗精煤、铁矿粉等物料会结成大体积的冻块,掉落的机械设备长杆状尖锐部件等也会出现在散装物料中,这些大体积硬结块物料以及长杆状尖锐铁器在输送带的下料口等处极易发生卡顿等现象,随着输送带纵向运转,这些异物会划伤、撕裂输送带。
此外,皮带长时间负重运行,连接处连接件磨损,易造成皮带连接断开等故障。
皮带传动机构等设备引起的问题主要有跑偏、打滑、托辊损坏等故障。其中皮带跑偏是最为常见的故障,危害性也比较大。皮带机架加工、制造、安装质量不好,滚筒或托辊安装位置不正,会导致皮带在运行时受力不平衡,皮带机的托辊不垂直于皮带中心线,机头、机尾滚筒偏离皮带中心线,皮带安装、运行过程中皮带松弛或张紧力不足,矿料分布不均匀导致皮带两端受到的张力不一样,皮带机运行过程中滚筒、托辊表面粘附物料,会使滚筒或托辊局部直径变大,引起皮带两侧张紧力不均匀以及运行中皮带的振动,以上等等原因都会引发皮带跑偏故障。
皮带的运量超载,超过皮带承载能力,皮带张紧力不够或者过松,造成摩擦力过大或者过小;皮带运行过程中长期磨损老化,造成皮带与滚筒之间摩擦力过小;皮带滚筒胶层磨完,导致摩擦力减小,驱动电机转速异常,造成滚筒以及输送带卡阻。以上故障都会造成皮带打滑,长时间的皮带打滑会引起皮带着火、设备损坏等问题。
钢铁、煤炭企业输送的精煤、原煤等物料,经常出现物料自燃的现象,导致物料受损,皮带烧坏,影响生产。此外设备运行也会造成皮带着火。一般情况下,滚筒发生打滑,会使滚筒表面温度升高,皮带打滑40 min,滚筒表面温度可达到320 ℃[1],皮带开始冒烟,形成明火;托辊发生故障不转造成皮带在托辊表面滑动,长期摩擦造成托辊表面温度升高,散热不好,也会引发皮带着火。
皮带打滑故障造成皮带未张紧,因此物料会越堆越多,导致出现堵料现象。异物堵塞受料斗,物料输送不顺畅,造成堵料、撒料。
对于输送皮带常见的撕裂、跑偏、打滑、堵料等故障,目前大多采用接触或者非接触检测开关进行检测。针对皮带撕裂状态检测,发展出了光电式、导电橡胶式、振动感应式、拉线式以及挡板式等多种撕裂检测开关。但是大部分撕裂开关只能在输送带撕裂穿透后才能动作,使得撕裂开关的检测作用十分有限。皮带跑偏故障主要采用光电检测开关、接触式位置检测开关等传感设备。接触式位置检测跑偏开关固定安装于皮带线两侧支架上,皮带跑偏时,皮带边沿挤压到开关滚轮达到一定程度,触发开关动作,检测到皮带处于跑偏状态。皮带打滑检测开关通过检测机尾从动轮转速,如果小于皮带设定运转速度,可以判定为皮带打滑。堵料检测是一个比较困难的问题,物料在溜槽内高速碰撞,容易碰撞到溜槽内部设置的堵料开关,造成不能有效检测堵料,且容易出现误报警。物料自燃引起火灾的检测,一般采用烟雾报警装置。
以上传统的开关检测方法都有一个普遍的问题,开关的可靠性和稳定性都比较差,比较容易受外部因素干扰,开关在轻微扰动下容易发生误动作,影响皮带机正常运行。在长皮带通廊或者室外运输条件下,火灾报警装置基本无法发挥作用。基于成本的因素,目前的开关一般不具有定位识别的功能,难以给故障点准确位置。
近年来,随着机器视觉等人工智能[2]技术的发展,已经有人工智能(AI)技术应用于设备故障以及产品表面检测。视觉检测技术应用于皮带检测中,主要是通过数字化相机和高速图像传输系统实时对皮带进行拍摄,利用计算机图像处理[3]和模式识别技术来检测皮带状态。相较于传统的皮带损坏检测方法,视觉检测方式具有检测效率高、检测准确率高、可检测多种皮带损坏状态等优点。计算机视觉处理技术[4]可以检测皮带的多种损坏情况,如长裂、横裂、接头断裂、边角断裂等。但是,视觉检测技术也存在设备投资大、技术复杂度高、对环境要求高等问题,对现场光线、污染、振动等因素有较高的要求,这就限制了该技术的适用范围。
目前有2D与3D两种视觉检测技术,两种技术适用于不同的检测环境与检测目标[5]。2D图像由包含X轴与Y轴坐标位置的像素点组成,3D图像增加了Z轴信息,即具备了深度信息。2D视觉系统通过相机拍摄平面照片,通过图像分析或对比识别对象,由于缺少深度信息,2D检测也存在无法测量形状、易受检测对象照明条件影响、对物体运动敏感等局限性,一般用于检测是否缺失、条形码和光学字符识别以及识别零件位置、检测角度和尺寸。3D视觉建立在2D视觉的成熟功能上,通过添加描述形状的第二层数据,通过3D摄像头采集视野空间内每个点位的三维座标信息,形成3D点云,充分还原物体本身形状,3D视觉可以测量产生2D系统不能产生的形状信息,可以测量与形状相关的特征,例如物体平直度、表面角度和体积;同时具有受环境、光线影响小等优点。
本文采用2D与3D视觉对皮带划痕与撕裂问题进行了检测,得到的结论是相比2D检测技术,3D检测技术在检测皮带撕裂、表面劣化趋势、跑偏等方面更具有优势,识别效果更好。图1是3D检测效果,检测对象是在1 m宽的皮带上,划出一个深度为0.3 cm、长度为15 cm的沟槽,测试3D检测效果良好。
基于以上研究,分析认为针对皮带故障所采用的传统开关检测、2D图像检测、3D图像检测等方法都存在各自的适应性与局限性。在开发皮带故障检测系统时,应统筹考虑检测目标、检测要求、成本造价、场景条件等多种因素,综合采用开关检测、图像检测等多种方式,构建造价合理、经济实用、检测效果良好的皮带故障综合监测系统。本文采用图像检测方式开发了边缘破损、跑偏、撕裂、划伤等皮带故障检测识别模型与软件,采用开关检测皮带打滑问题,综合开发了皮带故障检测系统,目前已经应用在生产现场。
输送皮带广泛分布于钢铁企业的各个生产工序,各皮带之间物理距离比较远,而且存在视频流等高频数据处理复杂、各类开关等多种检测传感器数据难融合等问题,图像识别检出率、识别准确率低劣化趋势无法把握等问题。为了实现厂内所有皮带的实时智能监控、检测、分析,适合采用基于工业互联网的云-边-端架构。前端实时采集皮带输送设备运行状态图像和多种检测开关信号,在边缘侧的物联计算网关和视频计算网关,对前端采集的信号进行处理,利用工业物联网平台形成边缘侧与云端应用的数据交换通道,在云端对边缘侧识别、分析、处理结果数据进行存储、分发和管理,并通过数据服务将故障诊断结果发送给PC端、移动端等设备,提供故障的查询、报警服务。
本文归纳总结了输送皮带常见故障及原因,比较了各类皮带运行故障的检测方法,采用图像处理技术对皮带表面异常进行智能识别,提出了统筹考虑多种因素、采用多种检测方式构建造价合理、经济实用、检测效果良好的皮带故障综合监测方法。开发了基于图像识别技术的皮带边缘破损、跑偏、撕裂、划伤等皮带故障检测识别模型与软件,结合开关检测方式,开发了皮带故障检测系统,应用于生产现场,效果良好。探讨了基于工业互联网的皮带综合检测平台架构。对于大型工矿企业输送皮带常见故障检测系统构建具有一定的参考意义。
  • 内蒙古自治区应用技术研究与开发资金计划资助项目(2022YFHH0124)
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  • 接收时间:2023-12-03
  • 首发时间:2025-10-23
  • 出版时间:2024-02-25
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  • 收稿日期:2023-12-03
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内蒙古自治区应用技术研究与开发资金计划资助项目(2022YFHH0124)
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    1 内蒙古新联信息产业公司,内蒙古 包头 014010
    2 内蒙古包钢钢联股份有限公司仓储中心,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
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
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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