[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.
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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()
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《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.

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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