摘要
运用模糊理论的故障征兆与故障原因之间的模糊关系,确定了BP神经网络的输入层和输出层,并结合水轮发电机组故障诊断具体实例建立神经网络的输入样本集,对神经网络进行训练,输入机组故障征兆向量,得出故障原因,从而验证了模糊神经网络的可行性与优越性。
This paper determines the input layer and output layer for BP Neural Network by using the fuzzy relationship between symptoms and causes of fault in the Fuzzy Theory. Combined with the eoncrete example of fault diagnosis of hydro-generating unit, it establishes the input sample set of neural network, and trains the neural network. Inputting the malfunction omen vector of hydro-generating unit, then the causes of fault were obtained. Finally, this paper verifies the feasibility and superiority of fuzzy neural network.
出处
《水力发电》
北大核心
2009年第8期56-58,共3页
Water Power
基金
四川省教育厅重点资助项目(0228993)
关键词
故障诊断
BP算法
模糊神经网络
水轮发电机组
fault diagnosis
BP algorithm
fuzzy neural network
-hydro-generating unit
作者简介
谭伦慧（1985-），女，贵州镇远人，硕士研究生，主要从事水轮机方面的研究．
