摘要
Abstract
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Key words
multi-sensor information fusion/belt conveyor/fault diagnosis/early diagnosis分类
矿业与冶金
