[1]吕晓伟,王占飞.基于多特征融合的煤矿带式输送机驱动系统状态评估方法研究[J].机械与电子,2026,44(07):46-52.
　LYU Xiaowei,WANG Zhanfe.Research on a State Assessment Method for the Drive System of Coal Mine Belt Conveyors Based on Multi-feature Fusion[J].Machinery & Electronics,2026,44(07):46-52.
点击复制

基于多特征融合的煤矿带式输送机驱动系统状态评估方法研究()
分享到：

《机械与电子》[ISSN:1001-2257/CN:52-1052/TH]

卷:
44
期数:
2026年07期
页码:
46-52
栏目:
智能检测
出版日期:
2026-07-25

文章信息/Info

Title:
Research on a State Assessment Method for the Drive System of Coal Mine Belt Conveyors Based on Multi-feature Fusion
文章编号:
1001-2257(2026)07-0046-07
作者:
吕晓伟; 王占飞
神东煤炭集团寸草塔二矿,内蒙古 鄂尔多斯 017209
Author(s):
LYU Xiaowei; WANG Zhanfe
(Cuncaota No.2 Mine of Shendong Coal Group,Ordos 017209,China)
关键词:
带式输送机; 驱动系统; 状态评估; 多特征融合
Keywords:
belt conveyor; drive system; condition assessment; multi-feature fusion
分类号:
TD528
文献标志码:
A
摘要:
针对煤矿带式输送机驱动系统运行状态难以综合评估的问题,提出一种基于多特征融合的状态评估方法。以寸草塔二矿31108运输顺槽带式输送机为研究对象,在80%负荷工况下,以额定参数和保护阈值为边界构建仿真样本,从电流、振动、温升和速度偏差4个维度选取5个特征指标,采用熵权法构建健康指数(HI),实现驱动系统状态量化表征。结果表明:HI随驱动系统状态劣化呈明显递减趋势,不同状态具有较好的分级特征;随机森林模型的宏平均F1值为95.79%,验证了所选多特征和状态划分结果的有效性。对比实验表明,多特征融合优于单一特征输入,随机森林模型在噪声扰动条件下具有较好的鲁棒性。
Abstract:
To address the challenge of comprehensively evaluating the operating state of drive systems in coal mine belt conveyors,this paper proposes a state assessment method based on multi-feature fusion. Taking the belt conveyor installed in the 31108 transport crossheading of Cuncao Tower No.2 Coal Mine as a case study,simulation samples were generated under 80% load conditions with boundaries defined by rated parameters and protection thresholds.Five feature indicators were selected from four dimensions:current,vibration,temperature rise,and speed deviation.The entropy weight method is then used to construct a Health Index (HI),enabling a quantitative representation of the drive system state.The results show that the HI exhibits a pronounced monotonic decline as the drive system deteriorates,yielding well separated grading characteristics across different health conditions.The random forest model achieves a macro average F1 score of 95.79%,verifying the validity of the selected multi-source features and the state classificationresults.Comparative experiments further reveal that multi-feature fusion significantly outperforms single-feature input,and that the random forest model has good robustness under noise perturbation.

参考文献/References:

[1] 杨丽文. 煤矿皮带机自动张紧装置结构设计与性能探究[J]. 机械管理开发, 2025, 40(12): 160-162.

[2] 楚金龙, 王伟京, 胡长对, 等. 长运距带式输送机动态启动特性的研究[J]. 煤矿机械, 2025, 46(1): 80-82.
[3] Alharbi F, Luo Suhuai, Zhang Hongyu, et al. A brief review of acoustic and vibration signal-based fault detection for belt conveyor idlers using machine learning models[J]. Sensors, 2023, 23(4): 1902.
[4] Gelman L, Abdullahi A O, Moshrefzadeh A, et al. Innovative conveyor belt monitoring via current signals [J]. Electronics, 2023, 12(8): 1804.
[5] Huang Yourui, Yuan Biao, Xu Shanyong, et al. Fault diagnosis of permanent magnet synchronous motor of coal mine belt conveyor based on digital twin andISSA-RF[J]. Processes, 2022, 10(9): 1679.
[6] 桂彬彬, 周建平, 万晓静, 等. 基于改进XGBoost的带式输送机驱动系统健康状态评估研究[J]. 煤炭技术, 2024, 43(8): 230-234.
[7] 孙鹏. 带式输送机的状态评价及故障识别技术研究[D]. 沈阳: 东北大学, 2023.
[8] 郑云龙. 基于BP神经网络的刮板输送机健康状态实时评估[J]. 煤矿机械, 2017, 38(6): 148-150.
[9] 李俊, 高超, 赵倩曦. 煤矿带式输送机电机振动故障分析及处理对策[J]. 智能矿山, 2024, 5(10): 47-50.
[10] 杨杰, 霍建军. 矿用带式输送机驱动控制研究[J].工矿自动化, 2025, 51(增刊1): 85-87.
[11] 王太平. 带式输送机动态特性分析与多驱动功率平衡控制策略研究[J]. 机械管理开发, 2025, 40(12):206-208.
[12] Mejbel B G, Sarow S A, Al-Sharify M T, et al. A data fusion analysis and random forest learning for enhanced control and failure diagnosis in rotating machinery[ J]. Journal of Failure Analysis and Prevention,2024, 24(6): 2979-2989.
[13] Zhao Yunsheng, Li Pengfei, Kang Yu, et al. A health indicator enabling both first predicting time detection and remaining useful life prediction: application to rotating machinery[J]. Measurement, 2024, 235: 114994.
[14] 杨春雨,曹博仕,张鑫,等.带式输送机系统故障诊断方法综述[J].工矿自动化,2023,49(6): 149-158.
[15] 刘湘楠,赵学智,何宽芳.圆柱滚子轴承振动信号时频特征提取及状态识别[J].振动工程学报,2022,35(4):932-941.
[16] MT 872—2000 煤矿用带式输送机保护装置技术条件[S].
[17] GB/T 6075.3—2011 机械振动 在非旋转部件上测量评价机器的振动 第3部分:额定功率大于15 kW、额定转速在120 r/min至15 000 r/min之间的在现场测量的工业机器[S].
[18] 中华人民共和国应急管理部. 煤矿安全规程: 应急管理部令第17号[M]. 北京: 中国法制出版社, 2025.
[19] 李伟伟,易平涛,李玲玉.综合评价中异常值的识别及无量纲化处理方法[J].运筹与管理,2018, 27(4):173-178.
[20] 王秀丽,何金龙.基于监测数据和改进AHP 熵权法的空间结构健康状态评估[J].空间结构,2024,30(3):34-43.
[21] 严彤彤,王冬,彭志科,等.基于谱幅融合广义健康指数的可解释装备退化评估优化模型研究进展[J].机械工程学报,2024,60(18):1-16.
[22] 李睿,徐为,王胤泽,等.数字航道系统进程的健康指数模型研究[J].中国水运,2024(3):49-51.
[23] 方匡南,吴见彬,朱建平,等.随机森林方法研究综述[J].统计与信息论坛,2011,26(3):32-38.
[24] 张浪,张迎辉,张逸斌,等.基于机器学习的通风网络故障诊断方法研究[J].工矿自动化,2022, 48(3):91-98.

相似文献/References:

[1]曹高生,李志强,蒋刚.矿用带式输送机跑偏控制系统研究[J].机械与电子,2019,(03):59.
　CAO Gaosheng,LI Zhiqiang,JIANG Gang.Research on Deviation Control System of Mine Belt Conveyor[J].Machinery & Electronics,2019,(07):59.
[2]孙 强,刘广毅,哈斯铁尔·艾列西,等.基于托辊裸露特征的输煤皮带跑偏检测方法[J].机械与电子,2026,44(04):47.
　SUN Qiang,LIU Guangyi,HASITIEER Ailiexi,et al.A Conveyor Belt Deviation Detection Method in Coal Handling Systems Based onthe Exposed Idler Rollers Feature[J].Machinery & Electronics,2026,44(07):47.
[3]王传奇,秦 伟,黄焕晟,等.基于HT-YOLO11的皮带跑偏智能识别及落料溜槽调节的主动防御策略[J].机械与电子,2026,44(08):35.
　WANG Chuanqi,QIN Wei,HUANG Huansheng,et al.Intelligent Recognition of Belt Deviation Based on HT-YOLO11 and Active Defense Strategy via Discharge Chute Adjustment[J].Machinery & Electronics,2026,44(07):35.

备注/Memo

备注/Memo:
收稿日期:2026-03-26
基金项目:国家能源集团科技创新项目(E210100363)
作者简介:吕晓伟 (1985-),男,河北邯郸人,工程师,研究方向为矿井智能化建设;王占飞 (1981-),男,内蒙古鄂尔多斯人,硕士,高级工程师,研究方向为矿井机电,通信作者,E-mail:wzf8235@126.com。
更新日期/Last Update: 2026-08-27