基于多传感器融合的皮带输送机故障早期诊断系统  

Belt Conveyor Fault Early Diagnosis System Based on Multi-sensor Fusion

作　　者：李洁明 Li Jieming(Shanxi Yitang Coal Industry Co.,Ltd.,Jiexiu,Shanxi 032000,China)

机构地区：[1]山西义棠煤业有限责任公司,山西介休032000

出　　处：《机电工程技术》2025年第3期187-190,共4页Mechanical & Electrical Engineering Technology

摘　　要：为精确、快速地对煤矿用皮带输送机故障进行早期诊断,从传感器选择与布置、数据处理与融合两个方面构建了基于多传感器融合的皮带输送机故障早期诊断体系,提出了振动、温度、张力和声音4种关键监测指标。采用卡尔曼滤波法建立了数据融合的数学模型,通过实验获得了皮带输送机在不同工况下振动频率、温度变化和张力分布3种变化规律。实验结果表明,该诊断系统能够实时监测皮带输送机的运行状态,准确诊断出包括轴承磨损、电机过载和皮带松弛在内的多种故障类型,并在故障发生前平均提前24 h发出预警,显著提高了故障预警的准确性和可靠性;实验数据显示,预处理阶段未对基础数据进行显著修正,初态数据优良,T1时刻振幅感应器的原始读数2.3 mm/s经过抑噪后精简至2.2 mm/s,展示了抑噪技术在去除随机波动方面的有效性。进一步应用卡尔曼滤波后,T1时刻的数据从2.2 mm/s优化至2.18 mm/s,证明了该技术在综合不同传感信息和提高数据准确性方面的关键作用。To achieve accurate and rapid early diagnosis of belt conveyor fault in coal mine,an early diagnosis system is construct based on multiple sensor fusion,four key monitoring indexes of vibration,temperature,tension and sound are proposed.A mathematical model of data fusion is established by the Kalman filter method,and three rules of vibration frequency,temperature change and tension distribution of belt conveyor under different working conditions are obtained.Test results show that the diagnostic system can monitor the running status of the belt conveyor in real time,accurately diagnose various fault types including bearing wear,motor overload and belt relaxation,and issue early warning 24 h before the fault occurs,which significantly improves the accuracy and reliability of the fault warning.The experimental data show that the basic data and the initial reading of the amplitude sensor at T1 moment is 2.3 mm/s,and 2.2 mm/s after noise suppression,showing the effectiveness of the noise suppression technology in removing random fluctuations.After further application of Kalman filtering,the data at the T1 moment is optimized from 2.2 mm/s to 2.18 mm/s,demonstrating the key role of the proposed technology in integrating different sensing information and improving the accuracy of the data.

关 键 词：多传感器信息融合 皮带输送机 故障诊断 早期诊断 

分 类 号：TD528.1[矿业工程—矿山机电]