1. College of Safety Science and Engineering,Liaoning Technical University,Huludao　 125105,China; 2. Key Laboratory of Mine Thermo-motive Disaster and Prevention,Ministry of Education,Liaoning Technical University,Huludao　 125105,China

将矿井发生巷道冒落变形、风门开关或者破损、风机性能下降、巷道延伸及报废、矿车运行、罐笼提升、煤仓放空等变化引起通风系统风量发生异常变化的现象称为阻变型故障,根据风量传感器感知的风量变化确定通风系统发生阻变型故障的网络拓扑位置及其等效风阻,对矿井通风安全智能化管理,提高通风系统的安全保障能力意义重大。通风网络具有较好的自适应性及鲁棒性,利用矿井通风仿真系统MVSS生成训练样本,分别构建了基于SVM的分类器和回归器对故障位置和等效风阻进行诊断。仅以风量为特征的通风系统SVM分类与回归问题是一个不适定问题,研究表明诊断的准确性与传感器的个数及布置位置的分散程度有关,与传感器所在巷道的网络拓扑灵敏度以及巷道风量大小无关。

The abnormal change of ventilation system air volume is also known as the resistance variant fault,and is caused by roadway falling deformation,air-door opening and closing or broken,fan performance declining,roadway ex- tension or scrap,tramcar running,cage lifting and coal silo emptying et al. The network topology location of ventilation variant fault and its equivalent wind resistance are determined according to the numerical changes of air volume sen- sor. The above-mentioned method holds great influence on intelligent mine ventilation management and security capa- bilities improvement. Ventilation network possesses better self-adaptability and robustness,the mine ventilation simula- tion system MVSS is used to generate the training samples. SVM-based classifiers and regressors are constructed re- spectively to diagnose the fault location and the equivalent wind resistance. Ventilation system SVM classification and regression,which is only characterized by air volume,is an ill-posed problem. This study shows that the accuracy of the diagnosis is related to the number of sensors and the degree of dispersion of the location,not to the network topology sensitivity of the roadway where the sensor is located and the roadway air volume.

LIU Jian,GUO Xin,DENG Lijun,et al. Resistance variant single fault source diagnosis of mine ventilation system based on air volume characteristic[J]. Journal of China Coal Society,2018,43(1):143-149.