采煤机运行状态数据分布式实时预测模型

Distributed real time prediction model of shearer operating state data

针对采煤机大量运行状态数据不能得到及时处理的问题,研究了基于Storm的采煤机运行状态数据分布式实时预测模型。结合采煤机实际运行状态数据,通过Hadoop分布式存储数据库模拟采煤机运行状态实时数据流;通过Storm分布式实时大数据处理框架处理大量采煤机运行状态时间序列数据,采用门控循环单元(GRU)作为预测模型,实现对采煤机运行状态数据的实时预测;结合各类数据的阈值设定,实现故障预警。以某矿综采工作面MG400930-WD电牵引采煤机的数据为例,取截割部电动机电流、截割部电动机温度、牵引部电动机电流、牵引部电动机转速、调高泵工作压力、调高泵工作转速、冷却水压、变频器电流8种监测数据作为实验数据,对预测模型进行训练和测试,结果表明:预测模型收敛速度较快,且拟合优度达到0.9以上;除冷却水压外,其余数据的预警准确率均达到95%以上;处理速度快,整个预警过程共10 s左右,可满足应用要求。

In order to solve the problem that a large amount of shearer operating state data cannot be processed in time,the distributed real-time prediction model of shearer operating state data based on Storm is proposed.Combined with the actual operating state data of the shearer,the Hadoop distributed storage database is used to simulate the real-time data flow of the operating state of the shearer.The Storm distributed real-time big data processing framework is used to process a large number of time series data of the operating state of the shearer.And Gate Recurrent Unit(GRU)is adopted as the prediction model to achieve real-time prediction of shearer operating state data.Combined with the threshold setting of various data,the model can realize fault warning.Taking the data of MG400930-WD electric traction shearer in fully mechanized working face as an example,eight kinds of monitoring data are used as experimental data to train and test the prediction model.The data include cutting part motor current,cutting part motor temperature,traction part motor current,traction part motor speed,height adjustment pump working pressure,height adjustment pump working speed,cooling water pressure and inverter current.The results show that the prediction model converges quickly,and the goodness of fit is above 0.9.Except for the cooling water pressure,the early warning accuracy rate of the remaining data is above 95%.The processing speed is fast and the whole early warning process is about 10 s in total,which can meet the application requirements.