Research On Load Prediction And Speed Control Of Scraper Conveyor

With the development of intelligent control technology and the underground mining environment of the coal mine,the realization of "less people" and "unmanned" mining on the working face is the key to safe and efficient production,it is also an important development direction of coal mine intelligent mining.Scraper conveyor is the main coal conveying equipment in the fully mechanized coal mining face.It accepts coal falling during the mining process of the coal mining machine,and the load has nonlinear and unstable characteristics,which is easy to cause the scraper conveyor to fail due to sudden load changes.This paper extracts characteristic amplitude information of the scraper conveyor current,introduces a convolution neural network prediction algorithm to predict the load of the scraper conveyor,and uses this as the basis for the speed control of the scraper conveyor,using fuzzy PID control to complete the scraper Conveyor speed adjustment.The specific research work is as follows:(1)Aiming at the problem of unbalanced-mining and transportation in coal mines and the requirements of intelligence,the composition principle of the scraper conveyor and the characteristics of the transmission system are analyzed,and the overall scheme of speed control based on the load prediction of the scraper conveyor is formulated;the shallow neural network algorithm and The deep neural network algorithm was analyzed,and the load forecasting algorithm of the scraper conveyor was selected.(2)By analyzing the mapping relationship between the load and current of the scraper conveyor,the amplitude information of the gear meshing frequency of the reducer is used as the characterization of the load characteristic of the scraper conveyor;the current data collected in the coal mine is used to remove the power frequency and other processing to extract the characteristic amplitude data information of the load,establish the load amplitude data set,and provide data support for the load prediction model of the scraper conveyor.(3)The network architecture and characteristics of the convolution neural network are analyzed,combined with the short-term characteristic properties of the scraper conveyor,a load prediction method for the scraper conveyor based on one-dimensional convolution is proposed;A scraper conveyor based on the convolution neural network is established;The load forecasting model verifies the validity of the model through comparative analysis of historical data and forecasting data,and provides a laboratory basis for the load forecasting research of scraper conveyors in coal mine.(4)Based on the load prediction of the scraper conveyor,the speed control system of the scraper conveyor is designed,and the amplitude feedback of the fuzzy PID controller is used,and the amplitude change of the meshing frequency is used as the input to realize the speed of the scraper conveyor;Simulink software is used to verify the performance of the fuzzy PID controller,and used particle swarm optimization to optimize its control parameters,making the control more accurate and efficient.