各类旋转机械中最常用的通用零部件就是滚动轴承,由于旋转精度一般都较高所以容易出故障。如果能通过轴承产生的异常信号检测出轴承故障,则可以有效减少经济损失。智能化滚动轴承状态监测利用相关的传感器进行振动信号的采集,将数据导入MATLAB程序中进行时域分析与频域分析,通过神经网络建立一个识别系统,可以对一个轴承工作状态振动信号的分析来辨别这个轴承是否发生故障。 The most commonly used universal parts of all types of rotating machinery are rolling bearings, which are prone to failure due to the generally high accuracy of rotation. If you can detect the bearing through the bearing abnormal signal failure, you can effectively reduce economic losses. Intelligent rolling bearing condition monitoring using the relevant sensors for vibration signal acquisition, the data into the MATLAB program for time domain analysis and frequency domain analysis, through the neural network to establish a recognition system, a working condition of the bearing vibration signal can be identified to identify This bearing is faulty.