摘要
针对神经网络在故障诊断中存在着输入属性维数多和数据量庞大的缺点,利用粗糙集理论对原始数据进行约简,并剔除其中不必要的属性,构建了优化的粗糙集-神经网络系统。实例分析表明,使用该系统能够减少故障诊断的时间。
To the condition of many input dimensions and lots of data in neural network fault diagnosis, some reductions of data are derived based on rough sets theory and unessential attributes are eliminated, an optimized rough set - neural network intelligent system is established. Through analyzing for instance, the time of fault diagnosis can be reduced by using the system.
出处
《轴承》
北大核心
2008年第2期39-42,共4页
Bearing
关键词
滚动轴承
故障诊断
粗糙集
神经网络
rolling bearing
fault diagnosis
rough sets
neural network
