电力系统保护与控制Issue(8)：90-94,5.
融合粗糙集与神经网络的燃气轮发电机组振动故障诊断方法
Fault diagnosis of gas turbine generator set by combination of rough sets and neural network
李永德 1李红伟 1张炳成 2杨洁 1刘灏颖 1张娇1
作者信息
- 1. 西南石油大学电气信息学院，四川 成都 610500
- 2. 新疆油田公司百口泉采油厂，新疆 克拉玛依 834000
- 折叠
摘要
Abstract
In view of the problem that fault diagnosis for gas turbine vibration generator set parameters is difficult to reflect the state of unit fault directly, a fusion of rough set and neural network for gas turbine generator set vibration fault diagnosis is presented. Rough sets theory is applied in reduction of the original features of the vibration signal characteristic value data to remove unnecessary attributes. An optimized neural network structure which is used to fault diagnosis of gas turbine generator set is established based on rough sets. The experimental results show that the method is effective and provides a new idea for gas turbine generator set vibration fault diagnosis.关键词
燃气轮发电机组/故障诊断/粗糙集/神经网络Key words
gas turbine generator set/fault diagnosis/rough set theory/neural network分类
信息技术与安全科学引用本文复制引用
李永德,李红伟,张炳成,杨洁,刘灏颖,张娇..融合粗糙集与神经网络的燃气轮发电机组振动故障诊断方法[J].电力系统保护与控制,2014,(8):90-94,5.
