摘要
该文提出了一种基于两个神经网络的凝汽器故障诊断方法。首先使用非线性主元分析神经网络进行特征提取,降低数据维数,既简化了诊断过程,又确保故障诊断精度。随后使用概率神经网络获取最终的故障诊断结果,该神经网络训练速度快,而且容易添加新的训练样本。最后将提出的方法用于某汽轮机组凝汽器故障诊断中,测试结果表明该方法行之有效,且易于工程实现。
An approach to diagnosing the faults in a condenser via two neural networks is presented in this paper. At first, the nonlinear principal component analysis neural networks （PCANN） is employed to extract main features from high dimension patterns. Not only is the diagnosing process simplified but also the diagnosing accuracy is ensured. Consequently, the probabilistic neural networks （PNN） is utilized to obtain the final diagnosed results. PNN can be trained quickly; moreover, the new trained samples can be added to PNN easily. Finally, the proposed scheme is applied to diagnose the faults in a condenser of a turbine unit, the diagnosis results show that it is effective and easy to be put into practice.
出处
《中国电机工程学报》
EI
CSCD
北大核心
2005年第18期104-108,共5页
Proceedings of the CSEE
基金
国家留学基金委项目(99330021)
国家电力公司电力行业青年促进费项目(SPQKJ015)
关键词
热能动力工程
主元分析神经网络
概率神经网络
凝汽器
故障诊断
Thermal power engineering
Principal component analysis neural networks （PCANN）
Probabilistic neural networks （PNN）
Condenser
Fault diagnosis
