本文已被：浏览 1572次 下载 3573次
Received:January 12, 2010 Revised:March 05, 2010
Received:January 12, 2010 Revised:March 05, 2010
中文摘要: 为了实现滚动轴承故障的快速检测，提出了一种基于神经网络和轴承振动信号时域指标的滚动轴承故障检测方法。采用振动信号的偏态、峭度、峰值和裕度作为BP神经网络的输入，用BP算法对网络进行了训练。实验结果表明，利用该方法可以有效实现滚动轴承故障的快速检测。
Abstract:A method of detection about rolling bearing faults based on BP neural network and time domain features of vibration signal is proposed to realize fast fault detection. The input vector of the BP neural network are skewness, kurtosis, crest and margin. The network is trained with BP algorithm. Experimental results show that with this method fast detection of rolling bearing faults can be realized effectively.
文章编号： 中图分类号： 文献标志码：
基金项目:湖南省2009年教育厅项目
| Author Name | Affiliation |
| XIE Ya | 湖南涉外经济学院 计算机学部 湖南 长沙 410205 |
| Author Name | Affiliation |
| XIE Ya | 湖南涉外经济学院 计算机学部 湖南 长沙 410205 |
引用文本：
谢雅.基于神经网络的滚动轴承故障快速检测方法.计算机系统应用,2010,19(9):224-226
XIE Ya.Fast Detection Method of Rrolling Bearing Faults Based on BP Neural Network.COMPUTER SYSTEMS APPLICATIONS,2010,19(9):224-226
谢雅.基于神经网络的滚动轴承故障快速检测方法.计算机系统应用,2010,19(9):224-226
XIE Ya.Fast Detection Method of Rrolling Bearing Faults Based on BP Neural Network.COMPUTER SYSTEMS APPLICATIONS,2010,19(9):224-226
