带式输送机托辊故障诊断及协同管控研究综述

邢震, 田野新, 包建军, 齐智峰, 周李兵, 叶柏松, 张蓉

邢震,田野新,包建军,等. 带式输送机托辊故障诊断及协同管控研究综述[J]. 工矿自动化,2025,51(3):39-53. DOI: 10.13272/j.issn.1671-251x.2025010005
引用本文: 邢震,田野新,包建军,等. 带式输送机托辊故障诊断及协同管控研究综述[J]. 工矿自动化,2025,51(3):39-53. DOI: 10.13272/j.issn.1671-251x.2025010005
XING Zhen, TIAN Yexin, BAO Jianjun, et al. Review on idler fault diagnosis and coordinated control in belt conveyors[J]. Journal of Mine Automation,2025,51(3):39-53. DOI: 10.13272/j.issn.1671-251x.2025010005
Citation: XING Zhen, TIAN Yexin, BAO Jianjun, et al. Review on idler fault diagnosis and coordinated control in belt conveyors[J]. Journal of Mine Automation,2025,51(3):39-53. DOI: 10.13272/j.issn.1671-251x.2025010005

带式输送机托辊故障诊断及协同管控研究综述

基金项目: 

江苏省科技成果转化专项资金项目(BA2022040);天地(常州)自动化股份有限公司科研项目(2024TY0001);中国煤炭科工集团有限公司科技创新创业资金专项项目(2021-2-GH004)。

详细信息
    作者简介:

    邢震(1987—),男,山东临沂人,副研究员,硕士,研究方向为智能矿山综合管控、灾害综合防控,E-mail:694826672@qq.com

    通讯作者:

    包建军(1975—),男,江苏如皋人,研究员,硕士,主要研究方向为煤矿通信定位与自动化,E-mail:bozjason@outlook.com

  • 中图分类号: TD634

Review on idler fault diagnosis and coordinated control in belt conveyors

  • 摘要:

    托辊作为带式输送机的关键部件,其故障频发严重影响煤矿生产效率与安全。目前国内外在托辊故障诊断技术和带式输送机管理控制策略方面开展了广泛研究,然而尚未形成一套被广泛认可且行之有效的监测与管控手段。通过分析托辊故障的类型及机理,指出井下带式输送机托辊故障诊断的特殊性及面临的挑战。梳理了托辊故障诊断技术及托辊故障后协同管控的研究现状:在故障状态感知技术方面,探讨了振动、声音、温度及图像信号感知技术的原理与应用;在数据处理及特征提取方面,探讨了各类信号的处理方法及特征提取策略;在故障识别方法方面,探讨了从传统方法到机器学习、深度学习及多源信息融合的托辊故障识别方法的技术演进过程;在托辊故障后协同管控方面,探讨了目前面临控制系统复杂性高、不同控制策略之间的兼容性差、状态监测数据的准确性和实时性难以保证等问题。基于上述研究,指出托辊故障诊断技术虽取得一定进展,但仍存在故障辨识度不高、覆盖范围有限、检测物理量单一、无法对故障进行分类及判断程度、未能评估故障可能引发的风险,以及缺乏全面的管控策略等问题,提出托辊故障诊断技术发展方向:从单一监测向多维度融合监测发展、从稀疏覆盖向密集全面覆盖迈进、从表象诊断向本质分析探究故障演化规律、从被动应对到主动预防的转变并推动从局部管控向全局协同管控的升级。

    Abstract:

    As a critical component of belt conveyors, idlers are prone to frequent failures, significantly impacting the efficiency and safety of coal mine operations. Extensive research has been conducted worldwide on idler fault diagnosis techniques and coordinated control strategies for belt conveyors. However, a universally accepted and effective monitoring and control framework is still lacking. This paper provides a comprehensive review of idler fault types and failure mechanisms, emphasizing the unique challenges associated with diagnosing faults in underground belt conveyors. The current state of research on idler fault diagnosis and post-failure coordinated control is systematically analyzed in four key areas: ① Fault State Perception Technologies: The principles and applications of vibration, acoustic, thermal, and image-based sensing technologies are discussed. ② Data Processing and Feature Extraction: Various signal processing methods and feature extraction strategies are examined. ③ Fault Identification Methods: The evolution of idler fault identification techniques is reviewed, ranging from traditional approaches to advanced machine learning, deep learning, and multi-source information fusion. ④ Post-Failure Coordinated Control: Challenges such as the high complexity of control systems, poor compatibility between different control strategies, and difficulties in ensuring the accuracy and real-time performance of condition monitoring data are highlighted. Despite notable advancements in idler fault diagnosis technologies, several challenges persist, including low fault identification accuracy, limited monitoring coverage, single-parameter detection, and the inability to classify faults or assess their severity. Furthermore, there is inadequate evaluation of potential fault-induced risks and a lack of comprehensive management strategies. Based on these findings, future research directions are proposed: advancing from single-parameter monitoring to multi-dimensional integrated monitoring, transitioning from sparse coverage to dense and comprehensive surveillance, shifting from surface-level diagnosis to in-depth analysis of fault evolution mechanisms, progressing from reactive responses to proactive fault prevention, and promoting the transformation from localized management to global coordinated control.

  • 我国厚松散层矿区分布在东北、华中及华东地区[1-2],且华东“两淮”矿区的厚松散层分布有丰富的含水层[3],厚松散含水层地质条件下煤层开采引起的地表沉陷表现为下沉系数偏大、土体受损区域范围广,研究厚松散含水层地质条件下覆岩破断及变形规律具有重要意义[4-5]

    一些学者对厚松散层下开采进行了研究。文献[6-7]通过实测数据研究发现采动后地表含水层失水固结,造成附加采动下沉,导致下沉系数偏大。文献[8-9]研究了含水层失水固结沉降机理,对含水层失水引起的地表下沉进行预计,可提高沉陷预计精度。文献[10]通过FLAC3D流−固耦合,得出含水层失水固结条件下最大下沉值较不失水时增加7%。文献[11]针对华东矿区厚松散层失水固结沉降引起的井壁破坏问题,研究了不同水位条件下地表失水固结沉降特征。文献[12]采用现场实测和相似材料模拟实验等手段,针对淮南矿区下沉值偏大现象进行了探究。文献[13]采用理论分析、现场实测及数值模拟等综合手段,探究厚松散层薄基岩综放开采条件下覆岩破断机理及该地层状态下的采动裂隙演化规律。文献[14]指出含水层失水沉降是导致下沉系数偏大的原因,以概率积分法推导出黏土体失水沉降的计算公式。文献[15]指出覆岩损伤是含水层失水的关键,通过数值模拟对采矿引起的覆岩损伤和失水过程进行了研究。文献[16]利用设计的相似材料实验装置对煤矿开采引起的厚松散层条件下承压含水层的失水规律和地表沉降规律进行了探究。文献[17]将基岩和松散层作为2种不同的介质,探究厚松散层矿区岩土体协同作用机理。上述研究对厚松散含水层地质采矿条件下覆岩破断及变形规律未作深入研究。针对上述问题,本文以淮南矿业(集团)有限责任公司潘四东煤矿11111工作面为工程背景,研究了厚松散层条件下含水层失水时覆岩破断及变形规律。根据研究区地层结构构建相似材料模型,采用数字摄影测量提取位移法记录覆岩观测线观测点的破断过程及移动变形情况,在W型及O型剪切应力拱的基础上,根据有效应力揭示厚松散含水层下开采地表下沉值偏大的内在机理,为类似地质采矿条件下安全生产提供理论依据。

    潘四东煤矿11111工作面倾向长度为145 m,实际回采走向长度为411 m,松散层厚度为336 m,属于典型的厚松散层。11111工作面上覆新生界下部含水层的成分是砂土和黏土的混合沉积物,富水性弱。顶部为粉砂岩、细砂岩、砂泥岩互层、黏土层,中部以砂岩为主,夹粉砂岩及泥岩,上部以泥岩、砂质泥岩为主。采煤方法为综采放顶煤一次全采高,顶板管理方式为全部垮落法。研究区地层结构见表1

    表  1  研究区地层结构
    Table  1.  A histogram of stratigraphic structure in the study area
    厚度(m)主要岩性
    新生界 第四系 全新统 40~130 浅黄、灰黄色粘土夹砂层
    更新统
    第三系上 上新统 0~152 灰绿、浅黄,多为粘土夹杂砂层
    中新统
    第三系下 渐新统 >205 浅灰、棕色砂泥岩互层,夹杂砂砾岩
    始新统
    中生界 白垩系 上统 >647 紫红色粉、细砂岩,砂砾岩
    下统 844 棕红粉砂岩、泥岩及细中粒砂岩
    侏罗系 上统 >637 凝灰质砂砾岩,凝灰岩和安山岩
    三叠系 下统 316~446 紫红色砂泥岩
    古生界 二叠系 上统 石千峰组 114~260 紫红杂色砂泥岩,夹石英砂岩及砂砾岩
    上石盒子组 316~566 灰绿色和浅灰色砂岩,底为石英砂岩且为含煤层
    下统 下石盒子组 106~265 灰色砂泥岩及其互层,底含粗砂岩,含煤层
    山西组 52~88 上部细砂岩、粗砂岩,下部深灰色泥岩,含煤层
    石炭系 上统 太原组 102~148 灰岩为主,夹泥岩及砂岩,含薄煤层
    奥陶系 中下统 400 中厚层为白云岩及白云质灰岩,夹灰岩
    寒武系 上统 土坝组 170~220 硅质结核白云岩,产Heleionellasp.化石
    固山组 9~78 白云岩,竹叶状灰岩,鲕状灰岩。
    中统 张夏组 146 鲕状灰岩,白云岩产Dameselluasp.化石
    徐庄组 190 棕黄砂岩,夹页岩及石灰岩
    毛庄组 152 多为砾状灰岩,鲕状灰岩和页岩
    下统 馒头组 215 紫色页岩夹灰岩,产Redlichasp.化石
    猴家山组 100~150 鲕状灰岩,孔洞灰岩和砂灰岩
    凤台组 10~100 页岩,砾岩
    上元古界 震旦系 徐淮群 九顶山组 117 白云岩,底部夹竹叶状灰岩
    倪园组 92 上部含泥白云岩,夹黄绿色钙质页岩,下部硅质条带白云岩
    四顶山组 137 厚层白云岩为主,产蠕形动物化石
    九里桥组 119 泥灰岩,砂灰岩
    四十里长山组 93 石英岩及钙质砂岩
    青白口系 八公
    山群
    刘老碑组 1050 页岩,泥灰岩,石英砂岩,底部铁质砂砾岩,含藻及疑源类化石
    伍山组
    张店组
    下元古界 凤阳群 1 171 千枚岩,白云岩,大理岩,白云质石英片岩,石英岩,含藻化石
    上太古界 五河群 >6 422 片麻岩,浅粒岩,变粒岩,斜长角闪岩互层,夹少量大理岩及磁 铁矿层,岩石混合岩化
    下载: 导出CSV 
    | 显示表格

    以潘四东煤矿11111工作面开采情况为模拟对象,选取研究区走向一剖面为原型进行相似材料模拟实验,根据模拟实验原理及实际情况选择1:100作为几何相似系数,建立相似材料模拟实验模型,如图1所示。11111工作面上覆岩层布设1、2号2条观测线,含水层上方布设3、4号2条观测线,每条观测线上布设23个观测点,共计92个观测点,模型开挖采用2 h模拟实际1 d。

    图  1  相似材料模拟实验方案及模型
    Figure  1.  Scheme and model of Similar material simulation experiment

    模型观测线具体设置如图2所示。开切眼和终采线均距边界55 cm,回采长度为190 cm,每2 h开采1次,每次推进10 cm,每次推进完成后记录覆岩变化和移动变形情况。

    图  2  相似材料模型观测线设置
    Figure  2.  Setting of observation lines for similar material models

    在相似材料模型正前方2~5 m处安放工业相机三脚架,通过移动和伸缩三脚架调整工业相机最佳位置,调整光圈、距离和灯光等直至工业相机记录的覆岩破断及移动变形效果最佳,然后固定三脚架,具体方法为在工业相机下方悬挂钩装置上配置一定质量物体,以保持实验过程中三脚架的稳定性(图3)。

    图  3  工业相机固定
    Figure  3.  Industrial camera fixing

    数字摄影测量提取位移法可快速记录覆岩观测线观测点的破断过程及移动变形,如图4所示。将工业相机获取的图像导入GetData数字化软件中,通过模型架上的 4 个控制点对图像进行校正,建立统一的坐标系统,获取每一幅图像中监测点的坐标。

    图  4  数字摄影测量提取位移法
    Figure  4.  Digital photogrammetry extraction displacement method

    按照开挖时间记录覆岩变化,在实验模型开挖过程中获取了大量的覆岩破断及移动变形图像,并用数字化仪软件对获取的图像进行处理。首先将像平面坐标与物平面坐标进行转换,然后进行数码相机镜头畸变校正,最后根据坐标转换参数和畸变转换参数来计算观测点的下沉值。

    随着工作面推进,煤层直接顶开始逐渐垮落,覆岩中主关键层失稳导致上覆岩层分层出现破断。

    沿着垮落带两侧底部作切线延伸至含水层,由相似材料模拟实验结果可知垮落角分别为57°和62°。根据覆岩变化过程可知覆岩两端并未发生破断,而在开切眼到采空区中部再到终采线的这部分空间中呈现拉伸−挤压−拉伸的受力状态,对应的剪应力变化为减小→增加→减小→增加,将该剪切应力变化称为W型剪切应力拱。在W型剪切应力拱作用下形成2条纵向的发育至含水层的主导水裂隙带。导水裂隙带的进一步发育引起含水层失水固结,在厚松散层重力作用下进一步压实,增加地表沉降的程度。含水层失水固结及断裂带移动挤压导致含水层与覆岩之间形成一个薄层空间。随着覆岩破断运动的加剧,在弯曲带和覆岩共同挤压下形成O型剪切应力拱,压缩薄层空间,导致地表下沉量偏大。覆岩运动及地表沉降过程如图5所示。

    图  5  覆岩运动及地表沉降过程
    Figure  5.  Process of overburden movement and surface settlement

    工作面开采产生的导水裂隙带发育至含水层时,打破了原有的孔隙水压力平衡状态,加剧了含水层间的流体在水力梯度作用下向采空区的垂向流动,在上覆岩层自重作用下,含水层孔隙体积减小,土体产生固结压缩。含水层失水沉降前覆岩的自重由孔隙水应力和土体颗粒有效应力2部分承受;失水后,含水层中孔隙水承担应力减小,土体颗粒承担应力增大,而工作面开采会打破原岩应力的平衡状态,致使覆岩与含水层产生水力联系,覆岩承担应力增大。在厚松散层地质采矿条件下,松散层厚度及采动程度是含水层失水固结的主要影响因素,松散层厚度影响含水层的水压力,采动程度影响导水裂隙带的发育高度,在孔隙水压力减小且导水裂隙带高度增加作用下,土体颗粒承受的有效应力增大。

    工作面开采引起覆岩应力平衡状态破坏,厚松散层含水层富含孔隙水,具有成岩晚、孔隙度大、泥质弱胶结、强度低等特点。地下开采使得工作面上方覆岩产生断裂破坏及移动变形,当导水裂隙带发育至厚松散层含水层时,含水层中的水就会沿采动裂隙流向采空区,造成岩土体中水位下降。在含水层所受总应力不变的情况下,孔隙水压力降低,导致含水层岩土体颗粒上的有效应力增加。

    $$ \sigma = \sigma ' + \left( {1 - \beta } \right)\mu $$ (1)

    式中:$ \sigma $为总应力;$ \sigma ' $为有效应力;$ \beta $为孔隙水压力;$ \mu $为岩石压缩系数比。

    在失水后,含水层固结压密含水层土体颗粒有效应力增大的情况下,含水层岩土体会产生附加下沉量,致使该条件下采矿活动引起的下沉量较大。

    $$ {\varepsilon _{\text{z}}} = {\alpha _{\text{v}}} \dfrac{\Delta \sigma }{{(1+{e_0}{\rm{)}}}} $$ (2)

    式中:$ {\varepsilon _{\text{z}}} $为岩土体失水下沉量;$ {\alpha _{\text{v}}} $为岩土体压密系数;$\Delta \sigma$为应力增量;$ {e_0} $为岩土体初始孔隙比。

    在模型达到稳态后,进行11111工作面的开采工作。在实验模型开采工作面的推进过程中,覆岩应力状态处于动态变化之中,11111工作面覆岩演化如图6所示。当工作面推进至距开切眼50 m位置时,顶板初次来压显现,开切眼处的上覆岩层发生破断并垮落至采空区,垮落带上方覆岩出现厚度为2 m的离层裂隙带,垮落带高度为24 m,垮落角为65°;当工作面推进至距开切眼90 m位置时,含水层失水固结及断裂带移动挤压含水层和覆岩之间形成一个薄层空间,较厚的松散层增加了含水层的孔隙水压力,导水裂隙带的进一步发育导致基岩上方含水层失水固结,在厚松散层的自重及附加应力作用下进一步压实薄层空间,增加了地表沉降程度;当工作面推进至距开切眼165 m位置时,在终采线外侧10.5 m含水层上方13 m处覆岩发生破断,覆岩产生长20 m的纵向离层裂隙,在距开切眼186 m位置处覆岩发生破断,该破断延伸至含水层下部;在实验模型工作面开采工作完成且覆岩达到稳态后,前垮落角为57°,后垮落角为62°,导水裂隙带高度为63 m,开切眼及停采线上方覆岩在应力集中作用下断裂产生纵向裂隙,开切眼及停采线上方垮落带区域内覆岩产生横向离层裂隙,纵向裂隙和横向离层裂隙加剧了覆岩与含水层间的水力联系。

    图  6  11111工作面覆岩演化
    Figure  6.  Evolution of overburden rock in 11111 working face

    观测实验模型工作面开采过程中观测点的移动变形量,并绘制成图,得到含水层失水状态下各观测线下沉量变化,如图7所示。

    图  7  含水层失水状态下各观测线的下沉量变化
    Figure  7.  The variation of subsidence of each observation line under water loss state of aquifer

    图7可知,不同观测线观测点的下沉量中,随着开采工作面的推进,各观测线覆岩下沉量逐渐增大,接近工作面的1号观测线覆岩下沉量最大,最大值为6 640 mm,大于工作面的开采厚度6 630 mm,原因是:原采空区受采动作用致使原先未完全垮落的岩层在其自重的影响下进一步破断压实;覆岩离层裂隙带发育至含水层,致使含水层失水固结及压实,且含水层中下渗的水量致使下部岩层的抗压强度减小,增加了岩层破断弯曲概率。1号及2号观测线在开切眼和终采线附近,其下沉量曲线走势基本类似,3号和4号观测线下沉量曲线走势基本吻合。工作面上方的1号及2号观测线下沉量曲线跳变一致,含水层上方的3号及4号观测线下沉量跳变同步,2号与3号观测线下沉量跳变异步,表明含水层对覆岩移动变形具有重要作用。

    (1) 根据相似材料模拟实验原理构建了以潘四东煤矿11111工作面为原型的厚松散含水层失水覆岩运动模型,根据模拟实验结果及含水层失水沉降理论分析,提出W型剪切应力拱及O型剪切应力拱的概念,解释了下沉量偏大的现象。

    (2) 通过相似材料模拟实验分析研究了该地质条件下覆岩损伤特征。在开采工作面推进过程中,覆岩应力状态处于动态变化之中,在工作面开采工作结束并进入稳态后,前垮落角为57°,后垮落角为62°,导水裂隙带高度为63 m,开切眼及终采线上方覆岩在应力集中作用下断裂,产生纵向裂隙,开切眼及终采线上方断裂带区域内覆岩产生横向离层裂隙,纵向裂隙和横向离层裂隙加剧了覆岩与含水层间的水力联系。

    (3) 通过相似材料模拟实验研究了该地质采矿条件下覆岩动态运动规律。随着开采工作面的推进,各观测线覆岩下沉量逐渐增大,接近开采工作面的1号观测线覆岩下沉量最大,大于工作面的开采厚度;1号及2号观测线在开切眼和终采线附近,其下沉量曲线走势基本类似且下沉量跳变一致;3号及4号观测线下沉量曲线走势基本吻合且下沉量跳变同步;2号与3号观测线下沉量跳变异步,表明含水层对覆岩移动变形具有重要作用。

  • 图  1   托辊故障分类分级精细化诊断及综合协同管控

    Figure  1.   Fine-grained fault classification, grading diagnosis and integrated coordinated control of idlers

    表  1   矿用带式输送机托辊故障诱因、现象、显著表征物理量及可能造成的影响

    Table  1   Causes, phenomena, significant physical characteristics, and potential impacts of idler faults in mining belt conveyors

    诱因 现象 显著表征
    物理量
    故障可能
    造成的影响
    筒体破损 摩擦阻力增大 温度、图像 不停机可能造成胶带损坏,停机可能造成外因火灾
    托辊轴承失效 内圈故障 固定频率的微弱冲击 振动、声音 不停机可能造成胶带跑偏
    外圈故障
    滚珠故障
    保持架故障
    密封性不足 卡死 温度
    筒体或主轴变形 频率不固定的较强烈冲击、卡死 振动、声音、温度、图像 不停机可能造成胶带损伤或胶带跑偏
    下载: 导出CSV
  • [1] 佟哲. 矿用带式输送机托辊远程故障诊断方法研究[D]. 徐州:中国矿业大学,2020.

    TONG Zhe. Research on remote fault diagnosis method of idler of mine belt conveyor[D]. Xuzhou:China University of Mining and Technology,2020.

    [2] 邱明权. 矿用带式输送机托辊健康监测方法研究[D]. 徐州:中国矿业大学,2018.

    QIU Mingquan. Study on health monitoring method of idler of mine belt conveyor[D]. Xuzhou:China University of Mining and Technology,2018.

    [3] 侯长波. 干涉型光纤振动传感信号解调与识别技术研究[D]. 哈尔滨:哈尔滨工程大学,2021.

    HOU Changbo. Research on demodulation and identification technology of interferometric optical fiber vibration sensing signal[D]. Harbin:Harbin Engineering University,2021.

    [4] 贾振安,赵显锋,高宏,等. 光纤布拉格光栅振动传感器研究[J]. 红外,2020,41(7):18-24.

    JIA Zhen'an,ZHAO Xianfeng,GAO Hong,et al. Research on fiber bragg grating vibration sensor[J]. Infrared,2020,41(7):18-24.

    [5] 张娟,张磊,程文华,等. 一种高灵敏度声表面波振动传感器的设计研究[J]. 仪器仪表学报,2023,44(10):100-111.

    ZHANG Juan,ZHANG Lei,CHENG Wenhua,et al. Research on the design of a high-sensitivity surface acoustic wave vibration sensor[J]. Chinese Journal of Scientific Instrument,2023,44(10):100-111.

    [6] 杜富瑞,陈国良,谷宝平,等. 基于WSN的多金属矿井下人机定位系统设计[J]. 金属矿山,2022(12):165-169.

    DU Furui,CHEN Guoliang,GU Baoping,et al. Design of personnel and vehicle positioning system in polymetallic mine based on WSN[J]. Metal Mine,2022(12):165-169.

    [7] 夏欣. 面向无线振动传感器短时振动信号的分析与诊断[D]. 马鞍山:安徽工业大学,2021.

    XIA Xin. Analysis and diagnosis of short-term vibration signal for wireless vibration sensor[D]. Maanshan:Anhui University of Technology,2021.

    [8] 王森,郭述文,刘秉坤. MEMS光纤振动传感器在大型电机设备异常监测场景的应用研究[J]. 中国设备工程,2024(17):172-174. DOI: 10.3969/j.issn.1671-0711.2024.17.071

    WANG Sen,GUO Shuwen,LIU Bingkun. Research on application of MEMS optical fiber vibration sensor in abnormal monitoring scene of large motor equipment[J]. China Plant Engineering,2024(17):172-174. DOI: 10.3969/j.issn.1671-0711.2024.17.071

    [9] 井庆贺,张启良,王增仁,等. 带式输送机中间段托辊故障检测方法研究[J]. 中国安全科学学报,2023,33(增刊2):41-48.

    JING Qinghe,ZHANG Qiliang,WANG Zengren,et al. Research on fault detection method for middle section idler of belt conveyor[J]. China Safety Science Journal,2023,33(S2):41-48.

    [10] 郭帅. 带式输送机托辊轴承分布式状态监测系统研制[D]. 淮南:安徽理工大学,2022.

    GUO Shuai. Development of distributed condition monitoring system for roller bearing of belt conveyor[D]. Huainan:Anhui University of Science & Technology,2022.

    [11] 张中盘,张明,时瑛,等. 皮带输送机托辊故障声源定位方法[J]. 噪声与振动控制,2024,44(1):142-147.

    ZHANG Zhongpan,ZHANG Ming,SHI Ying,et al. Sound source localization method for belt conveyor idler faults[J]. Noise and Vibration Control,2024,44(1):142-147.

    [12] 武国平. 带式输送机托辊故障检测方法[J]. 工矿自动化,2023,49(2):149-156.

    WU Guoping. Fault detection method for belt conveyor idler[J]. Journal of Mine Automation,2023,49(2):149-156.

    [13] 伍鹏. 基于麦克风阵列的带式输送机机组智能预警技术研究[D]. 北京:北京化工大学,2023.

    WU Peng. Research on intelligent early warning technology of belt conveyor unit based on microphone array[D]. Beijing:Beijing University of Chemical Technology,2023.

    [14] 吴文臻,程继明,李标. 矿用带式输送机托辊音频故障诊断方法[J]. 工矿自动化,2022,48(9):25-32.

    WU Wenzhen,CHENG Jiming,LI Biao. Audio fault diagnosis method of mine belt conveyor roller[J]. Journal of Mine Automation,2022,48(9):25-32.

    [15] 赵初峰. 基于音频分析技术的矿井带式输送机托辊故障诊断系统研究[J]. 煤炭技术,2023,42(2):200-202.

    ZHAO Chufeng. Research on fault diagnosis system of supporting roller of mine belt conveyor based on audio analysis technology[J]. Coal Technology,2023,42(2):200-202.

    [16] 赵新哲,杨金刚,任磊. 带式输送机托辊故障检测方法研究[J]. 山东煤炭科技,2023,41(4):135-137.

    ZHAO Xinzhe,YANG Jingang,REN Lei. Research on fault detection method of belt conveyor support roller[J]. Shandong Coal Science and Technology,2023,41(4):135-137.

    [17] 朱振. 带式输送机托辊运行状态在线巡检机器人关键技术研究[D]. 阜新:辽宁工程技术大学,2020.

    ZHU Zhen. Research on key technologies of on-line inspection robot for running status of belt conveyor roller[D]. Fuxin:Liaoning Technical University,2020.

    [18] 孙亮. 基于自供能无线传感器网络的托辊监测系统研究[D]. 徐州:中国矿业大学,2022.

    SUN Liang. Research on roller monitoring system based on self-powered wireless sensor network[D]. Xuzhou:China University of Mining and Technology,2022.

    [19] 宋克. TBM连续皮带机托辊巡检机器人系统设计与实现[D]. 石家庄:石家庄铁道大学,2023.

    SONG Ke. Design and implementation of inspection robot system for roller of TBM continuous belt conveyor[D]. Shijiazhuang:Shijiazhuang Tiedao University,2023.

    [20] 胡长斌. 基于视频数据的托辊异常检测研究[D]. 西安:西安科技大学,2021.

    HU Changbin. Research on roller anomaly detection based on video data[D]. Xi'an:Xi'an University of Science and Technology,2021.

    [21] 吉日格勒,柳尧,尚书宏. 带式输送机托辊故障算法研究[J]. 中国安全科学学报,2023,33(增刊2):195-201.

    JIRI Gele,LIU Yao,SHANG Shuhong. Research on fault detection algorithm of rollers of coal conveyer belts[J]. China Safety Science Journal,2023,33(S2):195-201.

    [22] 戴忠林. 带式输送机托辊轴承故障智能诊断与寿命预测研究[D]. 阜新:辽宁工程技术大学,2022.

    DAI Zhonglin. Research on intelligent diagnosis and life prediction of roller bearing fault of belt conveyor[D]. Fuxin:Liaoning Technical University,2022.

    [23] 李涛. 带式输送机运行状态智能监控系统研究[D]. 西安:西安建筑科技大学,2020.

    LI Tao. Research on intelligent monitoring system of belt conveyor running state[D]. Xi'an:Xi'an University of Architecture and Technology,2020.

    [24] 周毅炜. 光纤分布式传感系统在胶带机故障信号检测中的应用研究[D]. 成都:电子科技大学,2023.

    ZHOU Yiwei. Research on application of optical fiber distributed sensing system in fault signal detection of belt conveyor[D]. Chengdu:University of Electronic Science and Technology of China,2023.

    [25] 彭程程. 基于二阶瞬态提取变换的滚动轴承故障特征提取方法研究[J]. 机电工程,2021,38(10):1246-1252.

    PENG Chengcheng. Fault feature extraction method for rolling bearing based on STET[J]. Journal of Mechanical & Electrical Engineering,2021,38(10):1246-1252.

    [26] 李勇. 基于数据驱动的带式输送机轴承故障诊断技术研究[D]. 徐州:中国矿业大学,2021.

    LI Yong. Research on fault diagnosis technology of belt conveyor bearing based on data driving[D]. Xuzhou:China University of Mining and Technology,2021.

    [27] 仪继超. 基于分布式光纤传感系统的带式传输机故障监测技术研究[D]. 济南:齐鲁工业大学,2023.

    YI Jichao. Research on fault monitoring technology of belt conveyor based on distributed optical fiber sensing system[D]. Jinan:Qilu University of Technology,2023.

    [28] 曹贯强. 带式输送机托辊故障检测方法[J]. 工矿自动化,2020,46(6):81-86.

    CAO Guanqiang. Fault detection method for belt conveyor roller[J]. Industry and Mine Automation,2020,46(6):81-86.

    [29] 贺志军,李军霞,刘少伟,等. CEEMD−VMD与参数优化SVM结合的托辊轴承故障诊断[J]. 机械科学与技术,2024,43(3):402-408.

    HE Zhijun,LI Junxia,LIU Shaowei,et al. Roller bearing fault diagnosis combined CEEMD-VMD and parameter optimization SVM[J]. Mechanical Science and Technology for Aerospace Engineering,2024,43(3):402-408.

    [30] 邱园园. 多种时频图联合的托辊故障诊断方法研究[D]. 银川:宁夏大学,2022.

    QIU Yuanyuan. Research on fault diagnosis method of idler combined with multiple time-frequency graphs[D]. Yinchuan:Ningxia University,2022.

    [31] 李羽蒙,樊红. 基于MFCC特征与卷积神经网络的托辊故障诊断方法[J]. 武汉大学学报(工学版),2024,57(5):691-698.

    LI Yumeng,FAN Hong. A fault diagnosis method of roller based on MFCC features and convolutional neural network[J]. Engineering Journal of Wuhan University,2024,57(5):691-698.

    [32] 郭洁,井庆贺,闫寿庆,等,基于MFCC声音特征信号提取的托辊故障诊断[J]. 中国安全科学学报,2023,33(增刊2):116-121.

    GUO Jie,JING Qinghe,YAN Shouqing,et al. Roller fault diagnosis based on MFCC sound feature signal extraction[J]. China Safety Science Journal,2023,33(S2):116-121.

    [33] 伊鑫,杨明锦,杨林顺,等. 基于KNN与SVM两级综合健康指标的托辊故障诊断方法[J]. 选煤技术,2020,48(5):94-102.

    YI Xin,YANG Mingjin,YANG Linshun,et al. The KNN and SVM-based 2-level comprehensive health indicators diagnosis method for detecting the failure of belt conveyor’s idlers[J]. Coal Preparation Technology,2020,48(5):94-102.

    [34] 陶瀚宇,陈换过,彭程程,等. 基于MFCC−IMFCC混合倒谱的托辊轴承故障诊断[J]. 机电工程,2024,41(7):1215-1222.

    TAO Hanyu,CHEN Huanguo,PENG Chengcheng,et al. Fault diagnosis of idler bearings based on MFCC-IMFCC hybrid cepstral coefficients[J]. Journal of Mechanical & Electrical Engineering,2024,41(7):1215-1222.

    [35] 张启虎. 基于PCHIP−EWT声音处理技术的带式输送机故障诊断系统研究[D]. 曲阜:曲阜师范大学,2024.

    ZHANG Qihu. Research on fault diagnosis system of belt conveyor based on PCHIP-EWT sound processing technology[D]. Qufu:Qufu Normal University,2024.

    [36] 刘春,张明,董帆,等. 基于包络谱峭度的输送皮带托辊故障特征提取[J]. 机电信息,2023(7):59-63.

    LIU Chun,ZHANG Ming,DONG Fan,et al. Extraction of fault characteristics of conveyor belt roller based on envelope spectrum[J]. Mechanical and Electrical Information,2023(7):59-63.

    [37] 贺志军. 基于机器学习算法的带式输送机托辊故障诊断方法研究[D]. 太原:太原理工大学,2023.

    HE Zhijun. Research on fault diagnosis method of belt conveyor idler based on machine learning algorithm[D]. Taiyuan:Taiyuan University of Technology,2023.

    [38] 缪江华. 基于卷积神经网络的带式输送机托辊故障诊断[J]. 煤矿机械,2024,45(6):182-185.

    MIAO Jianghua. Fault diagnosis of belt conveyor roller based on convolutional neural network[J]. Coal Mine Machinery,2024,45(6):182-185.

    [39] 韩信. 基于声信号的托辊轴承故障诊断方法研究[D]. 淮南:安徽理工大学,2024.

    HAN Xin. Research on fault diagnosis method of idler bearing based on acoustic signal[D]. Huainan:Anhui University of Science & Technology,2024.

    [40] 刘玉良. 光纤测温中温度解调与去噪方法的研究[D]. 淮南:安徽理工大学,2010.

    LIU Yuliang. Research on temperature demodulation and denoising method in optical fiber temperature measurement[D]. Huainan:Anhui University of Science & Technology,2010.

    [41] 宁武霆,赵春菊,周宜红,等. 混凝土坝光纤测温噪声特征及降噪方法[J]. 水电能源科学,2021,39(6):73-76,80.

    NING Wuting,ZHAO Chunju,ZHOU Yihong,et al. Combined noise reduction model of distributed optical fiber temperature measurement data for concrete dam[J]. Water Resources and Power,2021,39(6):73-76,80.

    [42] 丁厚轩. 带式输送机关键部位异常温度监测系统研究[D]. 徐州:中国矿业大学,2023.

    DING Houxuan. Research on abnormal temperature monitoring system of key parts of belt conveyor[D]. Xuzhou:China University of Mining and Technology,2023.

    [43] 郭清华. 基于光纤测温技术的带式输送机托辊故障识别算法研究[J]. 煤矿机械,2018,39(8):157-160.

    GUO Qinghua. Research on roller fault identification algorithm of belt conveyor system based on fiber temperature measurement technology[J]. Coal Mine Machinery,2018,39(8):157-160.

    [44] 郭清华. 基于光纤测温的托辊轴温检测及热传导模型研究[J]. 矿业安全与环保,2020,47(1):66-69,74.

    GUO Qinghua. Research on roller shaft temperature detection and thermal conductivity model based on optical fiber temperature measurement[J]. Mining Safety & Environmental Protection,2020,47(1):66-69,74.

    [45] 王金凤. 基于多信息融合的带式输送机故障诊断研究[D]. 曲阜:曲阜师范大学,2021.

    WANG Jinfeng. Research on fault diagnosis of belt conveyor based on multi-information fusion[D]. Qufu:Qufu Normal University,2021.

    [46] 张俊. 基于红外热成像技术的托辊故障诊断系统研究[J]. 机械研究与应用,2023,36(4):174-178.

    ZHANG Jun. Research on idler fault diagnosis system based on infrared thermal imaging technology[J]. Mechanical Research & Application,2023,36(4):174-178.

    [47] 马宏伟,杨文娟,张旭辉. 基于红外热像的带式输送机监测与预警系统[J]. 激光与红外,2017,47(4):448-452.

    MA Hongwei,YANG Wenjuan,ZHANG Xuhui. Monitoring and warning system of belt conveyor based on infrared thermography[J]. Laser & Infrared,2017,47(4):448-452.

    [48] 井坤. 基于红外图像处理的带式输送机故障诊断系统研究[D]. 曲阜:曲阜师范大学,2021.

    JING Kun. Research on fault diagnosis system of belt conveyor based on infrared image processing[D]. Qufu:Qufu Normal University,2021.

    [49] 金学智. 基于红外图像的带式输送机故障预警方法研究[D]. 银川:宁夏大学,2021.

    JIN Xuezhi. Research on fault early warning method of belt conveyor based on infrared image[D]. Yinchuan:Ningxia University,2021.

    [50] 阮顺领,阮炎康,卢才武,等. 基于红外图像的矿石传送带托辊异常检测[J]. 黄金科学技术,2023,31(1):123-132.

    RUAN Shunling,RUAN Yankang,LU Caiwu,et al. Detection of ore conveyer roller based on infrared image[J]. Gold Science and Technology,2023,31(1):123-132.

    [51] 郭盈辉. 基于机器视觉的带式输送机托辊故障检测的研究[D]. 天津:天津工业大学,2022.

    GUO Yinghui. Research on fault detection of belt conveyor idler based on machine vision[D]. Tianjin:Tianjin Polytechnic University,2022.

    [52] 陈岩. 带式输送机传动滚筒轴承故障智能诊断[J]. 工矿自动化,2023,49(增刊1):56-59,137.

    CHEN Yan. Intelligent fault diagnosis of belt conveyor drive roller bearing[J]. Journal of Mine Automation,2023,49(S1):56-59,137.

    [53] 谢苗,孟庆爽,马苏宁,等. 分布式光纤传感系统托辊故障监测技术研究[J/OL]. 机械科学与技术:1-11[2024-12-06]. https://doi.org/10.13433/j.cnki.1003-8728.20240092.

    XIE Miao,MENG Qingshuang,MA Suning,et al. Research on fault monitoring technology for roller in distributed fiber optic sensing system[J/OL]. Mechanical Science and Technology for Aerospace Engineering :1-11[2024-12-06]. https://doi.org/10.13433/j.cnki.1003-8728.20240092.

    [54] 缪江华,苑静科,王文硕. 基于堆叠稀疏自编码和谱聚类分析的带式输送机托辊故障诊断[J]. 煤矿机械,2024,45(7):163-166.

    MIAO Jianghua,YUAN Jingke,WANG Wenshuo. Fault diagnosis of belt conveyor roller based on stacked sparse autoencoder and spectral clustering analysis[J]. Coal Mine Machinery,2024,45(7):163-166.

    [55] 梁堃,王驰. 基于分布式光纤声波传感器的带式输送机托辊故障监测方法[J]. 激光与光电子学进展,2023,60(9):276-284.

    LIANG Kun,WANG Chi. Roller fault monitoring of belt conveyor using distributed fiber-optic acoustic sensor[J]. Laser & Optoelectronics Progress,2023,60(9):276-284.

    [56] 刘勇. 基于声信号的带式输送机托辊故障特征分析[J]. 中国安全科学学报,2023,33(增刊2):13-17.

    LIU Yong. Fault characteristic analysis of belt conveyor rollers based on sound signal[J]. China Safety Science Journal,2023,33(S2):13-17.

    [57] 董乃吉. 基于声音信号的带式输送机托辊故障预警与定位研究[D]. 北京:北京化工大学,20240.

    DONG Naiji. Research on fault early warning and location of belt conveyor idler based on sound signal[D]. Beijing:Beijing University of Chemical Technology,2024.

    [58] 宋天祥,杨明锦,杨林顺,等. 基于谱聚类分析的托辊故障诊断[J]. 电子测量技术,2019,42(5):144-150.

    SONG Tianxiang,YANG Mingjin,YANG Linshun,et al. Fault diagnosis for roller based on spectral clustering analysis[J]. Electronic Measurement Technology,2019,42(5):144-150.

    [59] 董瑞佳. 基于迁移学习和DenseNet的带式输送机托辊故障检测方法[J]. 煤炭技术,2023,42(1):250-252.

    DONG Ruijia. Fault detection method of belt conveyor idler based on transfer learning and DenseNet[J]. Coal Technology,2023,42(1):250-252.

    [60] 张高祥. 基于声音信号的带式输送机托辊故障检测系统设计与研究[D]. 徐州:中国矿业大学,2022.

    ZHANG Gaoxiang. Design and research on fault detection system of belt conveyor roller based on sound signal[D]. Xuzhou:China University of Mining and Technology,2022.

    [61] 张伟,李军霞,吴磊,等. 基于1DCNN−ELM的带式输送机托辊轴承故障诊断研究[J]. 煤炭科学技术,2023,51(增刊1):383-389.

    ZHANG Wei,LI Junxia,WU Lei,et al. Research on fault diagnosis of idler bearing of belt conveyor based on 1DCNN-ELM[J]. Coal Science and Technology,2023,51(S1):383-389.

    [62] 张雄,武文博,李嘉禄,等. 基于波束形成及CNN−LSTM的托辊故障距离估计模型[J]. 噪声与振动控制,2024,44(5):114-119.

    ZHANG Xiong,WU Wenbo,LI Jialu,et al. Roller fault distance estimation model based on beamforming and CNN-LSTM[J]. Noise and Vibration Control,2024,44(5):114-119.

    [63] 陈维望,李军霞,张伟. 基于分支卷积神经网络的托辊轴承故障分级诊断研究[J]. 机电工程,2022,39(5):596-603.

    CHEN Weiwang,LI Junxia,ZHANG Wei. Hierarchical fault diagnosis of idler bearing based on branch convolutional neural network[J]. Journal of Mechanical & Electrical Engineering,2022,39(5):596-603.

    [64] 张皞正. 基于数据的托辊故障诊断方法的研究[D]. 沈阳:东北大学,2021.

    ZHANG Haozheng. Data-based research on fault diagnosis method of roller based[D]. Shenyang:Northeastern University,2021.

    [65] 白渊铭. 多源异构数据融合的带式输送机深度学习故障检测算法研究[D]. 太原:太原师范学院,2023.

    BAI Yuanming. Research on fault detection algorithm of belt conveyor based on multi-source heterogeneous data fusion[D]. Taiyuan:Taiyuan Normal University,2023.

    [66] 宋鹏飞,梅秀庄,尚志强,等. 基于数字孪生的带式输送机状态综合监测[J]. 煤矿机械,2024,45(1):196-198.

    SONG Pengfei,MEI Xiuzhuang,SHANG Zhiqiang,et al. Comprehensive monitoring of belt conveyor state based on digital twin[J]. Coal Mine Machinery,2024,45(1):196-198.

    [67] 高波,袁媛,岳伟,等. 基于机器学习的托辊故障等级评价模型研究[J]. 物流科技,2023,46(13):32-35.

    GAO Bo,YUAN Yuan,YUE Wei,et al. Research on fault grade evaluation model of roller based on machine learning[J]. Logistics Sci-Tech,2023,46(13):32-35.

    [68] 李士明,马新宇,郭依尉. 煤矿主运输皮带故障智能诊断与保护研究[J]. 中国矿业,2012,21(增刊1):592-595.

    LI Shiming,MA Xinyu,GUO Yiwei. The research on intelligent diagnosis and protection of the fault of the main transportation belt in coal mine[J]. China Mining Magazine,2012,21(S1):592-595.

    [69] 宋超. 掘进巷道带式输送机常见故障及处理措施[J]. 现代机械,2022(1):100-102.

    SONG Chao. Common faults and countermeasures of belt conveyor in excavation roadway[J]. Modern Machinery,2022(1):100-102.

    [70] 郑茂全. 煤矿带式输送机的优化控制与状态监测的研究[D]. 西安:西安科技大学,2015.

    ZHENG Maoquan. Study on optimal control and condition monitoring of belt conveyor in coal mine[D]. Xi'an:Xi'an University of Science and Technology,2015.

    [71] 赵炎. 基于电流检测的带式输送机故障预判和节能研究[D]. 秦皇岛:燕山大学,2015.

    ZHAO Yan. Research on fault prediction and energy saving of belt conveyor based on current detection[D]. Qinhuangdao:Yanshan University,2015.

    [72] 曹帅,王晓鹏,鲍康润. 煤矿胶带输送机常见故障处理技术研究[J]. 现代制造技术与装备,2024,60(9):166-168.

    CAO Shuai,WANG Xiaopeng,BAO Kangrun. Research on common fault handling technology of coal mine belt conveyor[J]. Modern Manufacturing Technology and Equipment,2024,60(9):166-168.

    [73] 罗伟刚. 皮带运输机在煤矿运输中的常见故障与处理[J]. 矿业装备,2023(3):176-178.

    LUO Weigang. Common faults and treatment of belt conveyor in coal mine transportation[J]. Mining Equipment,2023(3):176-178.

    [74] 爱保柱. 煤矿胶带输送机常见故障分析及处理[J]. 矿业装备,2022(2):262-263.

    AI Baozhu. Analysis and treatment of common faults of coal mine belt conveyor[J]. Mining Equipment,2022(2):262-263.

  • 期刊类型引用(6)

    1. 许时昂,吴海波,欧元超,席超强. 采煤沉陷松散层变形研究现状与分析. 科学技术与工程. 2024(17): 6999-7013 . 百度学术
    2. 寇保德,周乐勋,杨兴业. 基于SBAS-InSAR技术的水工环地质调查在某矿山地质构造沉降检测中的应用. 中国锰业. 2024(04): 70-75 . 百度学术
    3. 唐世界,陈攀. 特厚冲积层下开采覆岩移动及地表变形特征研究. 煤炭技术. 2023(09): 91-96 . 百度学术
    4. 阮学云,杨峥,陈涛. 巷道钻孔卸压中孔径对卸压效果的影响. 黑龙江科技大学学报. 2023(05): 661-666 . 百度学术
    5. 徐良骥,曹宗友,刘潇鹏,张坤,刘永琪. 基于分布式光纤的松散含水层失水沉降规律研究. 煤炭科学技术. 2023(10): 231-241 . 百度学术
    6. 庞冬冬,牛心刚,李传明,陈中琪,罗肖龙,林存傲. 承压水下自动采煤模拟实验系统设计与探索. 实验室研究与探索. 2022(06): 35-40 . 百度学术

    其他类型引用(3)

图(1)  /  表(1)
计量
  • 文章访问数:  114
  • HTML全文浏览量:  25
  • PDF下载量:  37
  • 被引次数: 9
出版历程
  • 收稿日期:  2025-01-02
  • 修回日期:  2025-03-23
  • 网络出版日期:  2025-03-12
  • 刊出日期:  2025-03-14

目录

/

返回文章
返回