综采工作面“三机”状态监测及故障诊断系统开发

Development of the condition monitoring and fault diagnostic system for Conveyors in the coal mining face

针对年产千万吨级大采高综采工作面对输送设备的要求,设计开发了一种基于LabVIEW的综采工作面"三机"状态监测及故障诊断系统。介绍了该系统的整体结构,重点阐述了故障的诊断方法,采用频域分析和小波分析分别提取了电动机定子电流和振动特征量,结合BP神经网络设计了基于多参量的故障诊断程序。试验结果表明,该系统为在地面准确监测井下工作面输送设备的运行状态及诊断故障类型,提供了一种行之有效的方法。

According to requirements of conveyors in the fully-mechanized coal face with annual production capacity of 10 million tons, a condition monitoring and fault diagnostic system based on the LabVIEW was designed. The paper briefly introduced the whole structure of the system,and expounded diagnostic methods in details. Based on the frequency domain analysis and wavelet analysis, characteristic qualities were extracted from stator current signal and vibration signal,combined with Back Propagation neural network, a Multi-parameter fault diagnostic system was designed. The experiment results indicate that the system is an effective tool to monitor the operation condition and diagnose faults types.