Research On Identification And Prediction Of Shearer Cutting Load Based On Multi-source Data Fusion

Drum load identification and prediction are the key problems to realize the coal-rock identification,the cutting automation and the fault diagnosis of transmission system of cutting unit.In this paper,by combining the theoretical analysis,the computer simulation and the experimental test,the drum load sensing method based on multi-sensor is designed,the model for multi-sensor data characteristics extraction and noise reduction is established,the drum load identification strategy based on multi-sensor information fusion is researched,the real-time perception and accurate prediction of shearer drum load is realized.The content of the paper are as follows:(1)Aiming at the problem that the cutting load of shearer drum can not be obtained,the system overall scheme for sensing the cutting load of shearer drum based on multi-sensor fusion is designed,the methods for pick load measuring,for drum torque measuring,for connecting pin shaft measuring and for rocker arm deformation measuring are invented,the multi-parameter synchronous acquisition and transmission based on multi-sensor is researched.The content mentioned above provides the basis for accurate perception of drum load.(2)Aiming at the problem that sensor installation positions can not be determined due to the complexity of the transmission system with multi-stage gears and rocker arm shell of cutting unit,the rigid-flexible coupling dynamics model of transmission system of shearer cutting unit is constructed,the interaction between the deformation of rocker arm shell and drum load is analyzed.By comparing the deformation law for key positions of rocker arm shell,the optimal installation positions for 12 strain sensors located in front and rear sides of rocker arm are obtained.The interaction between the transmission system for multi-stage gears of cutting unit and drum load is studied,the optimal installation position of drum torque sensor is determined to be the gear shaft 6 which is nearest to drum end.(3)Aiming at the problem that the large error and low precision in the experiment of shearer drum cutting with lessen ratio,according to the practical structure of shearer,the pick3-dimensional force sensor,the idler gear shaft load sensor,the load sensor of connecting pin shaft of rocker arm,the rocker arm strain sensor are developed,and the platform for data acquisition and transmission is constructed.The 1:1 shearer drum cutting experiment simulating underground work condition is performed in the Research and Development Center for National Energy Mine Machinery in Zhangjiakou Mine Machinery Co.Ltd.The experiment data of sensors in drum work process are obtained,which provides the basis for the identification and prediction of shearer cutting load based on multi-sensor data fusion.(4)Aiming at the amount noise interference included in drum experiment data,the model and method for characteristics extraction of drum measured data are proposed based on independent components and wavelet analysis.The analysis of sensor data in time-domain and frequency-domain is accomplished.The time-domain analysis result shows that the variation law of drum cutting load can be expressed by the sensor data.The frequency-domain analysis result shows that the first-order wave peak frequency of sensor data is 0.467 Hz,which is exactly the rotary frequency of the drum.The drum cutting load variation can be described by the frequency peaks of each sensor.(5)Aiming at the low measuring precision and poor stability of drum load identification with a single sensor,taking the drum load directly measured from pick load as the output sample,taking the measured data including the data of the idler gear shaft sensor,the data of the rocker arm connecting shaft sensor,the rocker arm deformation sensor as the input sample,the model for drum load identification and predication based on the deep neural network is established,and the model is verified by the experiment data.The verification result shows that the prediction accuracy of the model for drum 3-dimensional cutting load is over 83%,for drum torque is 95%,which shows the prediction model is of high accuracy.There are 104 diagrams,16 tables and 156 references.