摘要
为提升矿山智能监测仪表的测量精度,保障矿山生产安全,研究基于时间分析法的矿山智能监测仪表误差自动化补偿方法。采集矿山智能监测仪表历史测量数据,估算未经详细检测的仪表误差,去除负载率较低的数据,选取LSTM神经网络模型作为时间分析法,构建基于LSTM的误差检测模型,获取矿山智能监测仪表误差预测;通过建立智能监测仪表误差系数模型,利用贝叶斯理念和二进制变量期望值求解,获取误差补偿后的精准监测数据。实验结果显示,该方法可以检测到矿山多种智能监测仪表的监测误差,且补偿后的仪表误差可以控制在误差允许范围内。
In order to improve the measurement accuracy of mine intelligent monitoring instruments and ensure the safety of mine production,an automatic error compensation method of mine intelligent monitoring instruments based on time analysis method is studied.Collect the historical measurement data of mine intelligent monitoring instruments,estimate the instrument error without detailed detection,remove the data with low load rate,select LSTM neural network model as time analysis method,construct an error detection model based on LSTM,and obtain the error prediction of mine intelligent monitoring instruments.By establishing the error coefficient model of intelligent monitoring instrument,the accurate monitoring data after error compensation is obtained by using Bayesian concept and binary variable expectation value.The experimental results show that this method can detect the monitoring errors of various intelligent monitoring instruments in mines,and the compensated instrument errors can be controlled within the allowable error range.
作者
刘旭东
LIU Xudong(CHN Energy Xinjiang Energy Co.,Ltd.,Urumqi 830000,China)
出处
《自动化与仪表》
2025年第4期118-121,共4页
Automation & Instrumentation
关键词
时间分析法
智能监测仪表
误差检测
自动化补偿
误差系数模型
矿山生产
time analysis method
intelligent monitoring instrument
error detection
automated compensation
error coefficient model
mining production
