矿井通风系统阻变型故障诊断及风速传感器位置优化

Resistance variant fault diagnosis of mine ventilation system and position optimization of wind speed sensor

  • 摘要: 将矿井发生巷道冒落或变形、风门开关或者损毁、运输车辆堵塞等变化所引起的通风系统风量发生异常变化的现象称为矿井通风系统阻变型故障。根据故障发生后巷道风量的监测值,利用支持向量机(SVM)、遗传算法(GA)等人工智能方法可以诊断故障位置及故障量。由此引出的核心问题是如何确定井下安设传感器的最小数量以及安设位置?通过实际矿井的故障诊断模拟实验和金属矿山的现场试验提出了不需先验知识的基于邻域粗糙集属性约简算法的风速传感器安设位置优化方法,根据巷道风量对故障位置及故障量的重要度,得到的约简分支即为应当安设传感器的最优位置。研究表明传感器安设的数量与人为设定的邻域半径大小有关,邻域半径越大,传感器的安设数量就越多,故障诊断的准确度也就相对越高。提出用于描述λ取值、邻域半径、传感器安设位置,传感器所在分支风阻值、故障诊断准确率之间最优关系的扫帚模型,风阻较大的分支构成了扫帚把,风阻较小的分支构成了扫帚头,扫帚模型要求传感器应优先布置在风阻值较大的扫帚把上,扫帚模型的形状与λ取值有关,λ越大,邻域半径越小,扫帚头越小,亦即传感器数量越少。λ越小,邻域半径越大,扫帚头越大,亦即传感器数量越多。研究成果为风速传感器安设位置的确定提供了一种新的方法,实现了利用较少风速传感器监测数据对矿井通风系统阻变型故障的判识。

     

    Abstract: The abnormal change of the air volume in the ventilation system caused by the change of the roadway caving or deformation,the door opening or damage,the transportation vehicle blocking and so on is called the mine ventilation system resistance variant fault.According to the monitoring value of roadway air volume after the fault,the fault location and fault amount can be diagnosed by artificial intelligence methods such as support vector machine (SVM) and genetic algorithm (GA).The core problem is how to determine the minimum number and locations of sensors installed underground.Through the fault diagnosis simulation experiment of the actual mine and the field experiment of the metal mine,a method of optimizing the installation position of the wind speed sensor based on the attribute reduction algorithm of the neighborhood rough set without prior knowledge is proposed.According to the importance of the roadway air volume to the fault position and the fault amount,the reduction branch obtained is the optimal position of the sensor to be installed.The research shows that the number of sensors is related to the size of artificial neighborhood radius.The larger the neighborhood radius is,the more sensors are installed,and the higher the accuracy of fault diagnosis is.A broom model is proposed to describe the optimal relationship among the value of λ,neighborhood radius,sensor location,wind resistance value of the branch where the sensor is located and the accuracy of fault diagnosis.The branch with larger wind resistance forms the broom handle,and the branch with smaller wind resistance forms the broom head.The broom model requires that the sensor should be preferentially arranged on the broom handle with larger wind resistance value.The shape of the broom model is related to the value of λ.The larger the value of λ is,the smaller the neighborhood radius is,the smaller the broom head is,that is to say,the fewer the number of sensors.The smaller the neighborhood radius is,the larger the broom head is,that is to say,the more sensors there are.The research results provide a new method to determine the installation position of wind speed sensor,and realize the identification of mine ventilation system resistance variant fault by using less wind speed sensor monitoring data.

     

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