摘要
通过对滚动轴承振动信号的分析处理,提取能够反映轴承运行状态的特征量作为BP神经网络的输入,并用BP算法对该网络进行训练,利用神经网络的智能性来判断轴承所属的故障类型.仿真结果表明,该方法实用有效.
By analyzing and processing of the vibration signals of the ball bearing,the feature parameters which represent operating state of the ball bearing are extracted, and then input to the BP neural network to train the network with BP algorithm. The pattern of ball bearing failure can be identified with the intellectual ability of BP neural network. The simulation result shows that the method presented in this paper is practical and effective.
出处
《机械与电子》
2006年第4期9-11,共3页
Machinery & Electronics
