基于BP神经网络的矿井热动力灾害监测研究

Research on Mine Thermodynamic Disaster Monitoring Based on BP Neural Network

针对煤矿井下热动力灾害监测方式单一、易产生误判漏判等问题,提出一种基于BP神经网络的矿井热动力灾害监测方法。该方法根据矿井热动力致灾因素之间的相互耦合关系,利用多源数据融合的数据-特征-决策三级架构,首先在数据级利用卡尔曼滤波算法对特征参量数据进行归一化优化处理;然后在特征级采用BP神经网络对特征参量数据进行多源融合识别,获得煤层自燃和明火燃烧的特征识别结果;最后在决策级将特征识别结果与瓦斯浓度、煤尘浓度和特征信号持续时间相融合,得出最终监测判决结果。研究表明:该方法综合多参数融合判断,提高判决辨识矿井热动力灾害的准确率,能有效解决对矿井热动力灾害监测的误判漏判问题。

Aiming at the problems of the monitoring method of thermodynamic disaster in coal mines is single,which are prone to misjudgment and leakage,a method for monitoring thermodynamic disaster of mines based on BP neural network is proposed.According to the mutual coupling relation of the mine thermodynamic causal factors,the method uses three-level architecture for data-characteristics-decision of multi-source data fusion,first,the feature parameter data is normalized using the Kalman filter algorithm at the data level,and then,BP neural network is used for multi-source fusion recognition of feature parameter data at feature level to obtain the feature recognition result of coal seam spontaneous combustion and open flame combustion.Finally,the characteristic recognition results are fused with gas concentration,coal dust concentration and characteristic signal duration at the decision level to get the final monitoring verdict results.The research shows that this method integrates multi-parameter fusion judgments,improves the accuracy of judgment and identification of mine thermodynamic disaster,can effectively solve the problem of misjudgment and leakage of mine thermodynamic disaster monitoring.