大型回转机电设备采煤机故障智能诊断的研究

Research of Early Intelligent Diagnosis for Faults in Coal Excavator

采煤机是一个集机械、电气和液压为一体的大型复杂系统,工作环境恶劣,如果出现故障将会导致整个采煤工作的中断,造成巨大的经济损失。因此建立一个智能化监测系统,全面、综合地反映它的真实运行状态,并预告其故障发生发展的趋势是非常必要的。介绍了采用多传感器对采煤机的运行状态进行监测,通过小波包对监测信号进行分析,得到其特征频率信息,以达到对其早期故障的预测。

Coal excavator is a complicated system that consists of machinery , electric and hydraulic drive, etc. It works in adverse circumstances. Its stop by faults will cause whole mine system to break down. So it is necessary to set up a complete fault intelligent diagnosis system to accurately describe intact state1 forecast its faults and diagnose them. The paper introduces a method of using multisensor to detect running state and using the wavelet packet transform to analyse the signals. In the ends the character frequency information can be rotund and coal excavator＇s fault forecast achieved.