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现代矿业 ›› 2026, Vol. 42 ›› Issue (05): 240-243,248.

• 安全·环保 • 上一篇    下一篇

基于多源信息融合的煤矿井下瓦斯浓度在线监测与预警

刘 侃   

  1. 晋能控股装备制造集团
  • 出版日期:2026-05-25 发布日期:2026-06-18

Online Monitoring and Early Warning of Gas Concentration in Coal Mine Based on Multi-source Information Fusion

LIU Kan   

  1. Jinneng Holdings Equipment Manufacturing Group
  • Online:2026-05-25 Published:2026-06-18

摘要: 为解决煤矿瓦斯浓度监测精度低、响应速度慢,且单一传感器监测存在局限性的问 题,提出一种基于多源信息融合的煤矿井下瓦斯浓度在线监测方法。采用多传感信息数据融合技 术,整合型号为 IYFAS-A8FA8 的甲烷传感器、IYHFT-A7FG8 的温度传感器及 IGHFA-A7F8A 的风 量传感器采集的多源数据,先通过移动平均线处理法剔除异常数据,正交小波变换技术滤除噪声, 再采用双融合机制实现瓦斯浓度精准监测,并构建对应的实时监测与预警系统。以某煤矿为研究 对象开展实验验证,结果表明,该方法的监测灵敏度均在 95% 以上,最高可达 99.65%,相较于基于 BP 神经网络的监测方法平均高出 31.46%,相较于基于深度学习的监测方法平均高出 22.45%,监测 值与实际瓦斯体积分数贴合度高且处于置信区间内。提出的监测方法不仅有效提升了瓦斯浓度 监测的精度与可靠性,解决了传统监测方法的不足,还能通过分级预警及时规避安全风险,为煤矿 生产安全提供了有力的技术保障,同时为多传感信息融合技术在煤矿安全监测领域的应用提供了 实践参考,具有良好的应用前景。

关键词: 多传感信息数据融合, 瓦斯浓度, 在线监测, 温度传感器, 甲烷传感器, 风量传感器

Abstract: In order to solve the problems of low accuracy,slow response speed and limitations of sin⁃ gle sensor monitoring in gas concentration monitoring in coal mine,this paper proposes an online monitor⁃ ing method of gas concentration in coal mine based on multi-source information fusion. In this study, multi-sensor information data fusion technology is used to integrate multi-source data collected by meth⁃ ane sensor model IYFAS-A8FA8,temperature sensor model IYHFT-A7FG8 and air volume sensor model IGHFA-A7F8 A. Firstly,the abnormal data is eliminated by moving average line processing method,and the noise is filtered by orthogonal wavelet transform technology. Then,the double fusion mechanism is used to realize the accurate monitoring of gas concentration,and the corresponding real-time monitoring and early warning system is constructed. Taking a coal mine as the research object,the experimental verifi⁃ cation is carried out. The results show that the monitoring sensitivity of the method is above 95%,up to 99.65%,which is 31.46% higher than that of the monitoring method based on BP neural network,and 22.45% higher than that of the monitoring method based on deep learning. The monitoring value is highly consistent with the actual gas volume fraction and is within the confidence interval. The monitoring method proposed in this paper not only effectively improves the accuracy and reliability of gas concentration moni⁃ toring,but also solves the shortcomings of traditional monitoring methods. It can also avoid safety risks in a timely manner through graded early warning,providing a strong technical guarantee for coal mine produc⁃tion safety. At the same time,it provides a practical reference for the application of multi-sensor informa⁃ tion fusion technology in the field of coal mine safety monitoring,and has a good application prospect.

Key words: multi-source information fusion, gas concentration, on-line monitoring, temperature sen? sor, methane sensor, air volume sensor