Abstract
For the subjective limitation of gas sensor calibration in coal mines, a decision-making method for gas sensor calibration under monitoring failure was studied based on the Gauss process regression (GPR) and the correlation analysis of interval numbers. Based on the correlation characteristics of gas monitoring data of each monitoring point in the work face area in coal mine, the initial confidence interval of gas concentration in monitoring failure period was obtained by GPR, and then the confidence interval was further optimized by the correlation analysis of interval numbers. According to the correlation characteristics of monitoring data of each monitoring point, its similarity of dynamic variation tendency was measured by using Euclidean distance of interval numbers, and the optimal confidence interval was determined by calculating the correlation degree of interval numbers. The case study shows that making full use of the effective monitoring information of multiple monitoring points ensures the reliability of the initial confidence interval; the dynamic adjustment of model parameters in correlation analysis of interval number avoids the subjectivity defect of similar methods and further obtains the consistency between interval numbers’ reliability and correlation degree, which can ensure the effectiveness of the application of this method.
Highlights
Dingwen DongBased on the correlation characteristics of gas monitoring data of each monitoring point in the work face area in coal mine, the initial confidence interval of gas concentration in monitoring failure period was obtained by Gauss process regression (GPR), and the confidence interval was further optimized by the correlation analysis of interval numbers
Because of the harsh environment in underground coal mine, the gas concentration monitoring of sensor is affected by water vapor and dust, and its data transmission is affected by the electromagnetic interference and so on, so the monitoring data of gas concentration often distort
Based on the actual needs of sensor calibration decisionmaking in coal mines in China, the motivation of this paper is to provide a reliable and quantitative method for sensor calibration to make up for the defect of relying only on subjective judgment; it is studied by processing gas monitoring data combined with Gauss process regression (GPR) and interval number
Summary
Based on the correlation characteristics of gas monitoring data of each monitoring point in the work face area in coal mine, the initial confidence interval of gas concentration in monitoring failure period was obtained by GPR, and the confidence interval was further optimized by the correlation analysis of interval numbers. E case study shows that making full use of the effective monitoring information of multiple monitoring points ensures the reliability of the initial confidence interval; the dynamic adjustment of model parameters in correlation analysis of interval number avoids the subjectivity defect of similar methods and further obtains the consistency between interval numbers’ reliability and correlation degree, which can ensure the effectiveness of the application of this method
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