基于截齿振动及温度特性的煤岩识别研究  

Study on coal and rock identification based on vibration and temperature features of picks

作　　者：张强[1,2,3] 刘志恒[1] 王海舰[1] 田莹[1] 黄传辉[4] ZHANG Qiang;LIU Zhiheng;WANG Haijian;TIAN Ying;HUANG Chuanhui(School of Mechanical Engineering, Liaoning Technical University, Fuxin 123000, China;Engineering Research Center of Coal Resources Safety Mining and Clean Utilization, Liaoning Technical University, Fuxin 123000, China;State Key Lab of Mechanical Transmission, Chongqing University, Chongqing 400044, China;College of Mechanical and Electrical Engineering,Xuzhou lnsititute of Engineering,Xuzhou 221018, China)

机构地区：[1]辽宁工程技术大学机械工程学院,辽宁阜新123000 [2]辽宁工程技术大学煤炭资源安全开采与洁净利用工程研究中心,辽宁阜新123000 [3]重庆大学机械传动国家重点实验室,重庆400044 [4]徐州工程学院机电工程学院,江苏徐州221018

出　　处：《煤炭科学技术》2018年第3期1-9,18,共10页Coal Science and Technology

基　　金：国家自然科学基金资助项目(51504121);国家自然科学基金面上基金资助项目(51774161);辽宁省自然基金资助项目(201601324);机械传动国家重点实验室开放基金资助项目(SKLMT-KFKT-201515);煤炭资源安全开采与洁净利用工程研究中心资助项目(LNTU16KF02)

摘　　要：为实现采煤机截割过程中煤岩界面的精准识别,选取截割过程中截齿的振动信号和红外热像信号作为煤岩识别的特征信号,针对截割过程中截齿x、y、z三个方向的振动加速度信号、振动频谱图、齿尖红外闪温值和温度-频数图像进行实时采集,研究截齿振动信号、红外热像信号与不同煤岩比例试件之间的变化规律。研究结果表明:随着试件中岩石比例的增大,截齿振动加速度均值逐渐上升,频谱图对应的均方根值逐渐增大;截割试件过程中截齿齿尖产生点状闪温区,截割全岩试件时最高闪温值与高温区范围远大于截割全煤试件,温度-频数图像中最高温度所对应的频数逐渐上升。BP(Back-Propagation)神经网络的识别结果和测试样本的实际煤岩比例相符,能够对截割试件的煤岩比例进行准确识别,研究结果可为实现煤岩界面的精准识别提供重要的方法和手段。In order to realize an accurate identification of the coal and rock interface during the cutting process of the coal shearer,the vibration signals and infrared thermal image signals of the picks during the cutting process were selected as the signals of the coal and rock identification characteristics. According to the x,y and z three directional vibration acceleration signals of the picks during the cutting process,as well as the vibration spectrum,tip infrared flash temperature value and temperature-frequency image were timely collected,the variation law between the vibration signals and infrared thermal image signals of the picks and the different coal and rock percentage specimens were analyzed. The test results showed that with the rock percentage in the specimens increased,the average value of the pick vibration acceleration would be steadily increased and the correspondent root-mean-square value of the spectrum would be steadily increased. During the cutting process of the specimens,a point flash temperature area would be occurred on the tip of the pick. During the cutting of the full rock specimens,the max flash temperature value and high temperature area scale would be larger than the cutting of the full coal specimens and the correspondent frequency of the max temperature in the temperature-frequency image would be steadily increased. The identification results of the BP neural network would be same to the actual coal and rock percentage of the test samples and could accurately indentify the coal and rock percentage of the cutting spectrum. The study results could provide the important method and means to realize the accurate identification of the coal and rock interface.

关 键 词：采煤机 煤岩识别 截齿 振动信号 红外热像 BP神经网络 

分 类 号：TP277[自动化与计算机技术—检测技术与自动化装置]