多传感器数据融合在矿井安全监测中的应用  

Application of multi-sensor data fusion in mine safety monitoring

作　　者：张鹏鹏[1] 俞阿龙[2] 孙诗裕 徐冬平[1] 

机构地区：[1]南京工业大学自动化与电气工程学院,江苏南京211816 [2]淮阴师范学院物理与电子电气工程学院,江苏淮安223300

出　　处：《工矿自动化》2015年第12期5-8,共4页Journal Of Mine Automation

基　　金：国家自然科学基金资助项目(61350008)

摘　　要：针对矿井安全监测数据不准确、安全评判不可靠的问题,提出了一种基于多传感器数据融合的两级数据融合方法。一级融合先利用分批估计算法对单个传感器数据进行处理,提高数据采集的准确性,再采用自适应加权算法对同类多个传感器数据进行处理,获得矿井各环境参数的融合值;二级融合将矿井各环境参数的融合值与矿井安全标准特征向量进行灰色关联度分析,得到矿井安全状况的一致估计。实例验证了该方法的可行性和有效性。In view of problems of inaccurate mine safety monitoring data and unreliable safety evaluation,a two-level data fusion method based on multi-sensor data fusion was proposed.The first level fusion uses batch estimation algorithm to process single sensor data in order to improve accuracy of data collection,then adopts adaptive weighting algorithm to process similar multi-sensor data in order to obtain fused value of mine environmental parameters.The second level fusion makes grey correlation degree analysis between the fused value of mine environmental parameters and mine safety standard feature vector,so as to get consistent estimation of mine safety status.The example verifies feasibility and effectiveness of the method.

关 键 词：安全监测 多传感器数据融合 分批估计 自适应加权 灰色关联度 

分 类 号：TD76[矿业工程—矿井通风与安全]