摘要
滚动轴承在煤机设备中广泛应用,在恶劣工况下容易发生故障。为了能够及时准确地获取滚动轴承的运转状态,采用BP神经网络算法与小波函数对轴承振动信号进行分解,从而对滚动轴承进行状态监测以及故障诊断。实验结果表明,BP神经网络能够准确获得滚动轴承的运动状态及故障类型。
Rolling bearings are widely used in coal machine equipment.Under severe working conditions,they are prone to fault.In order to obtain the running state of the rolling bearing in time and accurately,used the BP neural network algorithm and wavelet function to decompose the bearing vibration signal,thus monitor the state of the rolling bearing and diagnose the fault.The experimental results show that the BP neural network can accurately obtain the motion state of the rolling bearing and fault type.
作者
吕楠
姚平喜
Lyu Nan;Yao Pingxi(College of Mechanical and Vehicle Engineering,Taiyuan University of Technology,Taiyuan 030024,China;Shanxi Key Laboratory of Precision Machining,Taiyuan 030024,China)
出处
《煤矿机械》
北大核心
2020年第8期172-173,共2页
Coal Mine Machinery
关键词
BP神经网络
振动
故障诊断
滚动轴承
BP neural network
vibration
fault diagnosis
rolling bearing
