摘要
针对可拓神经网络无法解决多故障诊断的问题,建立问题模型,将多故障诊断问题转化为多特征样本的聚类问题从模型结构和学习算法两个方面对ENN2进行改进,提出基于改进ENN2聚类算法的多故障诊断方法,并对其参数和时间复杂度进行分析采用工程实例对所提出的方法进行验证,结果表明,所提出的方法能够解决离线的多故障诊断问题,且得到的诊断模型可用于在线状态监控。
For the problem that multi-fault diagnosis can not be solved by the extension neural network, a problem model is built, and the multi-fault diagnosis problem is transformed into the clustering problem for multi-attribute samples. ENN2 is improved from two faces of the model structure and learning algorithm, and the multi-fault diagnosis method based on the improved ENN2 clustering algorithm is proposed with the analysis of parameters and time complexity. The proposed method is verified by an engineering instance. The results show that the method can resolve the offline multi-fault diagnosis problem, and the obtained diagnosis model can also be applied to online fault monitoring, so it has a wide application prospect.
出处
《控制与决策》
EI
CSCD
北大核心
2015年第6期1021-1026,共6页
Control and Decision
基金
武器装备预研基金项目(9140A27020212JB14311)
"泰山学者"建设工程专项经费项目
关键词
多故障诊断
可拓神经网络
改进ENN2聚类算法
状态监控
multi-fault diagnosis
extension neural network
improved ENN2 clustering algorithm
condition monitoring
