基于振动与声发射融合的采煤机截割部故障诊断研究

采煤机截割部传动齿轮的工作状态影响着传动系统的工作效率。对齿轮故障监测与诊断进行研究,采用CATIA建立故障齿轮模型,利用仿真软件ADAMS与COMSOL仿真齿轮啮合瞬间产生的振动与声发射信号,对信号进行特征提取,采用BP神经网络对采煤机截割部齿轮故障进行诊断。仿真结果表明,振动与声发射融合对微小齿轮裂纹的识别具有较高准确性,对采煤机故障诊断具有一定的指导意义。

Research on Fault Diagnosis of Cutting Part of Shearer Based on Fusion of Vibration and Acoustic Emission

The working status of the transmission gear of cutting part of shearer affects the working efficiency of the transmission system. Research was carried out on the gear fault monitoring and diagnosis. The fault gear model was established by CATIA, the vibration and acoustic emission signals generated at the moment of gear meshing was simulated by the simulation software ADAMS and COMSOL. Carried out the feature extraction of the signal, used BP neural network to carry out the fault diagnosis of the shearer cutting part gear. The simulation results show that the vibration and acoustic emission fusion has high accuracy in the identification of micro gear cracks, which has certain guiding significance for the fault diagnosis of shearer.