基于BP神经网络的滚动轴承故障监测研究

Rolling Bearing Fault Monitoring Research Based on the BP Neural Network

滚动轴承是机械设备中最常用的零件之一,它能否正常运行关系到整台机器的安全,所以对滚动轴承进行故障诊断具有重大的意义。本文搭建状态监测系统平台采集正常轴承和故障轴承的振动信号,根据特征选取原则,提取时域和频域特征并将特征值归一化。根据已知状态的轴承特征值训练BP神经网络,之后利用已建立的网络识别未知状态的轴承,经过试验验证,该方法得到了非常好的监测效果。

The rolling bearing is one of the most common parts in machinery and equipment. Its normal running is related to the safety of the whole machine. So the fault diagnosis of rolling bearing is of great significance. In this paper, the platform system to collect normal bearing and the fault bearing vibration signals is established. According to the principle of feature selection, the time domain and frequency domain features are extracted and the eigenvalues are normalized. BP neural network has been trained based on the bearing characteristics of known state. Bearing of unknown state is identified by using the established network, and it is proved that this method has got a very good effect.