1.School of Mechanical Engineering, Liaoning Technical University, Fuxin , China;2.Engineering Research Center of Coal ResourcesSafety Mining and Clean Utilization, Liaoning Technical University, Fuxin , China;3.State Key Lab of Mechanical Transmission,ChongqingUniversity,Chongqing China;4.College of Mechanical and Electrical Engineering，Xuzhou Insititute of Engineering,Xuzhou ,China

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.

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)；