采煤机关键部位故障监测及分析

Fault Monitoring and Analysis of Key Parts in Coal Mining Machines

预测性维护对采煤机正常作业尤为重要,能够有效防止重大安全事故和人员伤亡,为此,提出了基于监测数据定性和定量分析的采煤机关键部位预测性维护方法。利用离线及在线监测数据对采煤机关键零部件的位置、姿态、轨迹等信息进行定性分析,进而构建一种由自编码器(AE)和深度双向门控循环单元(Bi-GRU)相结合的预测模型,以预测采煤机关键部位的剩余使用寿命,并进行定量分析。以采煤机摇臂为例,基于AE Bi-GRU预测模型预测其剩余使用寿命,结果表明,AE Bi-GRU预测模型有效提高了采煤机摇臂的故障预测准确性。

Predictive maintenance is particularly important for the normal operation of coal mining machines,which can effectively prevent major safety accidents and casualties,for this reason,a predictive maintenance method for key parts of coal mining machines based on qualitative and quantitative analysis of monitoring data is proposed.The offline and online monitoring data can be used to qualitatively analyze the position,attitude,trajectory and other information of the key parts of the coal mining machine,and then construct a prediction model combined with an auto-encoder(AE)and a deep bi-directional gated recirculation unit(Bi-GRU)in order to predict the remaining service life of the key parts of the coal mining machine,as well as quantitatively analyze it.Taking the coal mining machine rocker arm as an example,the remaining service life is predicted based on the AE Bi-GRU prediction model,and the results show that the AE Bi-GRU prediction model effectively improves the fault prediction accuracy of the coal mining machine rocker arm.