摘要
Abstract
Aiming at the long operation time and harsh working environment of coal mining electromechanical equipments in Dongqu Mine,which lead to frequent machine equipment failures,traditional methods reflect the working status of equipment through real-time temperature monitoring data.However,considering that temperature is a lagging indicator,the equipment has already malfunctioned before parameter abnormalities,which cannot achieve the preventive purpose.A long short-term memory neural network model(LSTM)model is proposed and used to model the temperature time series,and ultimately determine the working status of equipment by the difference between actual temperature and calculated temperature,in order to prevent equipment failure.Numerical experiments show that the method of using LSTM model for temperature prediction of electromechanical equipment is feasible,and the accurate prediction model should select the temperature data of the first 24 hours as the independent variable.When there is a significant difference between the theoretical calculation value and the actual value,the cutting part,cooling system,and lubrication system of the equipment should be monitored and inspected in a timely manner.关键词
煤矿机电设备/温度监测/时间序列数据/LSTMKey words
coal mine electromechanical equipment/temperature monitoring/time series data/LSTM分类
矿山工程
