矿用设备的振动信号数据分析

Vibration Signal Data Analysis of Mining Equipment

矿用设备的机械臂是具有挠性结构的部件。在采掘工作时,机械臂受重载、强冲击作用,影响设备的采掘效率和工作的稳定性从而引发设备故障。矿用设备工作环境复杂,振动信号的采集和故障分析工作的难度较大。本文以采煤机摇臂为例,为测试采掘类设备机械臂的振动信号,设计了模拟采煤机摇臂振动信号的实验台;实验采集了摇臂在各种工况下的振动信号,建立了摇臂横向振动数据分析的网络模型。应用神经网络进行数据分析,拟合了摇臂横向振动模型,大大减少人工分析的误差,为矿用设备工作状态监测与故障预判提供数据依据。

The mechanical arm of mining equipment is a component with flexible structure.During mining work,the mechanical arm is subjected to heavy load and strong impact,which affects the mining efficiency and working stability of the equipment and leads to equipment failure.Mining equipment work environment is complicated,vibration signal acquisition and fault analysis work is more difficult.Taking the rocker arm of shearer as an example,in order to test the vibration signal of the mechanical arm of mining equipment,a test platform simulating the vibration signal of the rocker arm of shearer is designed.The vibration signals of the rocker arm under various working conditions are collected experimentally,and the network model for analyzing the transverse vibration data of the rocker arm is established.The lateral vibration model of rocker arm is fitted by neural network,which greatly reduces the error of manual analysis and provides data basis for mining equipment working condition monitoring and fault prediction.