Article(id=1321897365326381438, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1321897310649434924, articleNumber=null, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1776355200000, receivedDateStr=2026-04-17, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793094491559, onlineDateStr=2026-10-27, pubDate=1790265600000, pubDateStr=2026-09-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793094491559, onlineIssueDateStr=2026-10-27, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793094491559, creator=13701087609, updateTime=1793094491559, updator=13701087609, issue=Issue{id=1321897310649434924, tenantId=1146029695717560320, journalId=1225396423026438145, year='2026', volume='46', issue='9', pageStart='1', pageEnd='346', issueExtLink='null', onlineDate='null', pubDate='1790265600000', pubDateStr='2026-09-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1793094478523, creator='13701087609', updateTime=1793094618231, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1321897896698561428, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1321897310649434924, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1321897896698561429, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1321897310649434924, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=244, endPage=256, ext={EN=ArticleExt(id=1321897365523513727, articleId=1321897365326381438, tenantId=1146029695717560320, journalId=1225396423026438145, language=EN, title=Fault Diagnosis Method for Planetary Gear of Mine Hoist Based on Multi-Scale Acoustic-Vibration Fusion, columnId=null, journalTitle=Mining Research and Development, columnName=null, runingTitle=null, highlight=null, articleAbstract=

To address the problem that the planetary gears in the gearbox of mine hoists operate under long-term low-speed, heavy-load, and alternating-load conditions, and that single acoustic or vibration signals are easily affected by propagation path, measuring point position and background noise, leading to insufficient feature extraction of fault signals, a fault diagnosis method based on point-symmetric acoustic-vibration fusion and ARFM-Net is proposed. Firstly, an acoustic-vibration synchronous acquisition test system for the planetary gearbox was established, and the differences and complementarities of acoustic and vibration signals in expressing fault information were analyzed. Then, Variational Mode Decomposition (VMD) and Feature Mode Decomposition (FMD) were adopted to process the two types of signals according to their respective characteristics. The effective modal components containing major fault information were selected and mapped into a polar coordinate feature map by using the point-symmetric graph method to realize the image-level fusion of multi-scale acoustic-vibration information. Finally, an ARFM-Net diagnostic network composed of Adaptive RepLK and Frequency-aware Moga was constructed. An adaptive large-kernel convolution was used to enhance the extraction capability of local fault details and long-range texture correlations, and a frequency-aware multi-level gated feature aggregation was used to highlight fault-related information and suppress background redundant textures. The experimental results of five types of planetary gear fault states show that the average recognition accuracy of the proposed method reaches 98.72%, and it shows favorable diagnostic performance in both comparative and ablation tests. This method can effectively improve the fault-recognition accuracy of planetary gears in the gearbox of hoists under complex working conditions, and provide technical support for intelligent monitoring of operation status of hoist transmission systems in mines.

, authors=Tianyi Wang1, 2, Jintao Yang1, 2, Xitao Wang1, 3, Guang Li1, 2, 4, Rixin Xie1, 5, Yong Zhang1, 2, Zhuojun Li1, authorsList=Tianyi Wang, Jintao Yang, Xitao Wang, Guang Li, Rixin Xie, Yong Zhang, Zhuojun Li, authorCompany=null, correspAuthors=Jintao Yang, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1321897365766783360, articleId=1321897365326381438, tenantId=1146029695717560320, journalId=1225396423026438145, language=CN, title=基于多尺度声振融合的矿用提升机行星齿轮故障诊断方法, columnId=1226462295946085268, journalTitle=矿业研究与开发, columnName=矿山机电与矿业智能化, runingTitle=null, highlight=null, articleAbstract=

针对矿井提升机齿轮箱行星轮长期处于低速、重载及交变载荷工况,单一声音或振动信号易受传播路径、测点位置和背景噪声影响,导致故障信号特征提取不充分的问题,提出一种基于点对称声振融合与ARFM-Net的故障诊断方法。首先,搭建行星齿轮箱声振同步采集试验系统,分析声音与振动信号在故障信息表达方面的差异性与互补性;随后,根据两类信号的特征分别采用变分模态分解(VMD)和特征模态分解(FMD)进行分解,选取包含主要故障信息的有效模态分量,并利用点对称图方法将其映射为极坐标特征图,实现多尺度声振信息的图像级融合;最后,构建由Adaptive RepLK和Frequency-aware Moga组成的ARFM-Net诊断网络,利用自适应大核卷积增强局部故障细节与长距离纹理关联的提取能力,并通过频率感知多阶门控特征聚合,以突出故障相关信息、抑制背景冗余纹理。5类行星轮故障状态试验结果表明,所提方法的平均识别准确率达到98.72%,且在对比试验和消融试验中均表现出较好的诊断性能。该方法可有效提升复杂工况下提升机齿轮箱行星轮故障识别的准确性,为矿山提升机传动系统运行状态智能监测提供技术支撑。

, authors=王天翼1, 2, 杨锦涛1, 2, 王西涛1, 3, 李广1, 2, 4, 谢日昕1, 5, 张勇1, 2, 李卓军1, authorsList=王天翼, 杨锦涛, 王西涛, 李广, 谢日昕, 张勇, 李卓军, authorCompany=null, correspAuthors=杨锦涛, authorNote=

王天翼(2000—),男,湖南湘潭人,硕士,助理工程师,主要从事矿山机电设备的研究与开发。E-mail:

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杨锦涛(1985—),男,湖南湘潭人,硕士,高级工程师,主要从事矿山机电设备的研究与开发。E-mail:
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王天翼(2000—),男,湖南湘潭人,硕士,助理工程师,主要从事矿山机电设备的研究与开发。E-mail:

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王天翼(2000—),男,湖南湘潭人,硕士,助理工程师,主要从事矿山机电设备的研究与开发。E-mail:

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基于多尺度声振融合的矿用提升机行星齿轮故障诊断方法
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王天翼 1, 2 , 杨锦涛 1, 2 , 王西涛 1, 3 , 李广 1, 2, 4 , 谢日昕 1, 5 , 张勇 1, 2 , 李卓军 1
矿业研究与开发 | 矿山机电与矿业智能化 2026,46(9): 244-256
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矿业研究与开发 |矿山机电与矿业智能化 2026 , 46 (9) : 244 -256
基于多尺度声振融合的矿用提升机行星齿轮故障诊断方法
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王天翼(2000—),男,湖南湘潭人,硕士,助理工程师,主要从事矿山机电设备的研究与开发。E-mail:

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王天翼(2000—),男,湖南湘潭人,硕士,助理工程师,主要从事矿山机电设备的研究与开发。E-mail:

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王天翼1, 2 , 杨锦涛1, 2 , 王西涛1, 3, 李广1, 2, 4, 谢日昕1, 5, 张勇1, 2, 李卓军1
作者信息
  • 1.长沙矿山研究院有限责任公司,湖南 长沙 410012
  • 2.金属矿山安全技术国家重点实验室,湖南 长沙 410012
  • 3.战略性稀有金属矿产高效开发国家技术创新中心,湖南 长沙 410012
  • 4.深部金属矿安全智能开采国家矿山安全监察局重点实验室,湖南 长沙 410012
  • 5.国家金属采矿工程技术研究中心,湖南 长沙 410012
通讯作者:
杨锦涛(1985—),男,湖南湘潭人,硕士,高级工程师,主要从事矿山机电设备的研究与开发。E-mail:
作者简介:

王天翼(2000—),男,湖南湘潭人,硕士,助理工程师,主要从事矿山机电设备的研究与开发。E-mail:

Fault Diagnosis Method for Planetary Gear of Mine Hoist Based on Multi-Scale Acoustic-Vibration Fusion
Tianyi Wang1, 2 , Jintao Yang1, 2 , Xitao Wang1, 3, Guang Li1, 2, 4, Rixin Xie1, 5, Yong Zhang1, 2, Zhuojun Li1
Affiliations
  • 1.Changsha Institute of Mining Research Co., Ltd., Changsha, Hunan 410012, China
  • 2.State Key Laboratory of Safety Technology of Metal Mines, Changsha, Hunan 410012, China
  • 3.National Technological Innovation Center for Efficient Development of Strategic Rare Metal Mineral Resources, Changsha, Hunan 410012, China
  • 4.Key Laboratory of National Mine Safety Administration for Safe and Intelligent Mining of Deep Metal Mines, Changsha, Hunan, 410012, China
  • 5.National Engineering Research Center for Metal Mining, Changsha, Hunan 410012, China
出版时间: 2026-09-25
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针对矿井提升机齿轮箱行星轮长期处于低速、重载及交变载荷工况,单一声音或振动信号易受传播路径、测点位置和背景噪声影响,导致故障信号特征提取不充分的问题,提出一种基于点对称声振融合与ARFM-Net的故障诊断方法。首先,搭建行星齿轮箱声振同步采集试验系统,分析声音与振动信号在故障信息表达方面的差异性与互补性;随后,根据两类信号的特征分别采用变分模态分解(VMD)和特征模态分解(FMD)进行分解,选取包含主要故障信息的有效模态分量,并利用点对称图方法将其映射为极坐标特征图,实现多尺度声振信息的图像级融合;最后,构建由Adaptive RepLK和Frequency-aware Moga组成的ARFM-Net诊断网络,利用自适应大核卷积增强局部故障细节与长距离纹理关联的提取能力,并通过频率感知多阶门控特征聚合,以突出故障相关信息、抑制背景冗余纹理。5类行星轮故障状态试验结果表明,所提方法的平均识别准确率达到98.72%,且在对比试验和消融试验中均表现出较好的诊断性能。该方法可有效提升复杂工况下提升机齿轮箱行星轮故障识别的准确性,为矿山提升机传动系统运行状态智能监测提供技术支撑。

矿井提升机  /  行星齿轮  /  声振融合  /  点对称图  /  大核卷积  /  故障诊断

To address the problem that the planetary gears in the gearbox of mine hoists operate under long-term low-speed, heavy-load, and alternating-load conditions, and that single acoustic or vibration signals are easily affected by propagation path, measuring point position and background noise, leading to insufficient feature extraction of fault signals, a fault diagnosis method based on point-symmetric acoustic-vibration fusion and ARFM-Net is proposed. Firstly, an acoustic-vibration synchronous acquisition test system for the planetary gearbox was established, and the differences and complementarities of acoustic and vibration signals in expressing fault information were analyzed. Then, Variational Mode Decomposition (VMD) and Feature Mode Decomposition (FMD) were adopted to process the two types of signals according to their respective characteristics. The effective modal components containing major fault information were selected and mapped into a polar coordinate feature map by using the point-symmetric graph method to realize the image-level fusion of multi-scale acoustic-vibration information. Finally, an ARFM-Net diagnostic network composed of Adaptive RepLK and Frequency-aware Moga was constructed. An adaptive large-kernel convolution was used to enhance the extraction capability of local fault details and long-range texture correlations, and a frequency-aware multi-level gated feature aggregation was used to highlight fault-related information and suppress background redundant textures. The experimental results of five types of planetary gear fault states show that the average recognition accuracy of the proposed method reaches 98.72%, and it shows favorable diagnostic performance in both comparative and ablation tests. This method can effectively improve the fault-recognition accuracy of planetary gears in the gearbox of hoists under complex working conditions, and provide technical support for intelligent monitoring of operation status of hoist transmission systems in mines.

Mine hoist  /  Planetary gear  /  Acoustic-vibration fusion  /  Point-symmetric graph  /  Large-kernel convolution  /  Fault diagnosis
王天翼, 杨锦涛, 王西涛, 李广, 谢日昕, 张勇, 李卓军. 基于多尺度声振融合的矿用提升机行星齿轮故障诊断方法. 矿业研究与开发, 2026 , 46 (9) : 244 -256 .
Tianyi Wang, Jintao Yang, Xitao Wang, Guang Li, Rixin Xie, Yong Zhang, Zhuojun Li. Fault Diagnosis Method for Planetary Gear of Mine Hoist Based on Multi-Scale Acoustic-Vibration Fusion[J]. Mining Research and Development, 2026 , 46 (9) : 244 -256 .
  • 湖南省科技创新计划项目(2026AQ2015)
  • 战略性稀有金属矿产高效开发国家技术创新中心科技专项计划项目(GC2026KJ070004)
  • 应急管理部重点科技计划项目(2025EMST080101)
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2026年第46卷第9期
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  • 接收时间:2026-04-17
  • 首发时间:2026-10-27
  • 出版时间:2026-09-25
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  • 收稿日期:2026-04-17
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湖南省科技创新计划项目(2026AQ2015)
战略性稀有金属矿产高效开发国家技术创新中心科技专项计划项目(GC2026KJ070004)
应急管理部重点科技计划项目(2025EMST080101)
作者信息
    1.长沙矿山研究院有限责任公司,湖南 长沙 410012
    2.金属矿山安全技术国家重点实验室,湖南 长沙 410012
    3.战略性稀有金属矿产高效开发国家技术创新中心,湖南 长沙 410012
    4.深部金属矿安全智能开采国家矿山安全监察局重点实验室,湖南 长沙 410012
    5.国家金属采矿工程技术研究中心,湖南 长沙 410012

通讯作者:

杨锦涛(1985—),男,湖南湘潭人,硕士,高级工程师,主要从事矿山机电设备的研究与开发。E-mail:
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2种不同金属材料的力学参数
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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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