凝汽设备模糊神经网络故障类别诊断模型

MODEL FOR DIAGNOSING FAULT SORTS OF THE CONDENSER SYSTEM BASED ON FUZZY NEURAL NETWORKS

火电厂汽轮机凝汽设备故障较多 ,且故障原因复杂。在对凝汽设备故障类别详细分析的基础上 ,建立了基于模糊神经网络的凝汽器故障类别诊断模型。该模型结合了模糊逻辑与人工神经网络 (ANN)的优点 ,采用了先进的批处理自适应变尺度优化学习算法 (MDFP) ,减少了计算工作量 ,使故障诊断迅速 ,准确。仿真试验表明 。

Directing against the problems concerning condenser system faults in thermal power plant are large in amount and complex in causes, a model for diagnosing fault sorts for condenser system based on fuzzy neural networks has been established on the basis of analysing the fault sorts of the condenser system in detail. Combining advantages of fuzzy logic with that of artificial neural network (ANN), the said model has adopted advanced MDFP study algorithm, decreasing amount of calculation work, making the fault analysis to be rapid and accurate. Simulation test shows that the effectiveness of fault sorts analysis by using the said model being good.