摘要
针对当前水电机组故障原因复杂,实际监测数据量大,采用神经网络方法进行机组故障诊断存在网络结构复杂、训练时间长、诊断困难的问题,文章将粗糙集理论引入到水电机组故障诊断中,提出了基于粗糙集理论与RBF神经网络相结合的水电机组故障诊断方法。在保持分类能力不变的前提下,用粗糙集理论对故障信息进行约简处理,然后用RBF神经网络对预处理后的故障信息进行诊断,使神经网络的输入神经元数目明显减少,其结构得以简化。通过对某电站实测机组数据进行离线故障诊断,证明该诊断方法有效提高了机组故障诊断的效率和准确性。
Due to the excessive data of monitoring and the complexity of fault reason for hydroelectric units, the problems exist in neural network when it is used for the fault diagnosis of hydroelectric units, such as the complex structure,the long training time and the difficult diagnosis. The rough set theory is introduced and the fault diagnosis method for hydroelectric units based on rough set ＆ RBF neural network is presented. The fault information of hydroelectric units is reduced by the rough set theory on the basis of classifying capability unchanged,then the information is diagnosed by RBF neural network,which not only decreases the number of the network input nerve cells effectively, but also predigests the network structure. The application of an example proves that the proposed method can improve the accuracy and the efficiency of fault diagnosis of hydroelectric units.
出处
《西北农林科技大学学报（自然科学版）》
CSCD
北大核心
2007年第7期223-226,230,共5页
Journal of Northwest A&F University(Natural Science Edition)
基金
国家自然科学基金重点项目(90410019)
陕西省自然科学基础研究计划项目(2006D13)
