Research on Sensor Correction Method for Monitoring in Early Warning System of Coal Spontaneous Combustion. Issue 2 (May 2020) - Deng, Jun; Liang, Ce; Chen, Weile; Zhao, Jiaxiang; Yao, Hanwen
Abstract: According to statistics, there are as many as 4000 coal spontaneous combustion accidents every year, causing heavy casualties and property losses. By accurately detecting the change of index gas concentration, it can provide reliable criteria for identifying and warning the early hidden dangers of coal spontaneous combustion. The improvement of sensor precision is the basis of intelligent monitoring. Modification of wireless multi-parameter sensor used in coal mine is of great significance for accurate monitoring of gas concentration in coal mine. In order to modify the wireless multi-parameter sensor, the high and low temperature experiments of the sensor were carried out at -5∼45°C, and the real values of O2 concentration at 15%, 21%, 25% and CO concentration at 175ppm, 250ppm and 375ppm were measured. Measurement data was Non-linear fitting and correction by using SVM (Support Vector Machine), BP neural network and Elastic Network regression method. The experimental results show that the Elastic Network regression compensation reduces the O2 sensor from the original maximum mean absolute percentage error (MAPE) of 18.4% to 0.52%, and the average MAPE decreases from 13.89% to 0.21%. Using the SVM nonlinear compensation, the CO sensor is reduced from the original maximum MAPE of 43.2% to 1.6%, and the average MAPE is reduced from 21.38% to 1.4%. Elastic Network regression compensation excellent achieves nonlinear compensation of O2 sensor. The SVM better realizes the nonlinear compensation of the CO sensor. Through non-linear compensation, wireless multi-parameter sensor can better meet the requirements of coal mine gas concentration monitoring.