中文摘要：

针对当前专家系统知识获取瓶颈的难题，提出了基于神经网络与遗传算法的汽轮机数据挖掘方法．将汽轮机故障历史数据首先进行模糊化及离散化处理，接着构造一个多层的前向神经网络，然后通过教师示教的方式训练构造好的神经网络，最后进行基于遗传算法的神经网络优化．以神经网络为知识本体，提出了汽轮机故障诊断分类规则的挖掘算法，实现了基于神经网络与遗传算法的汽轮机数据挖掘和故障诊断仿真系统，其诊断正确率达到了84％．实验表明该方法可行，对汽轮机故障诊断系统的设计具有借鉴意义和深入研究的价值．

英文摘要：

Aimed at overcoming shortages of some current knowledge attaining methods, a novel approach for data mining of steam turbine based on neural network and genetic algorithm is brought forward. The historical fault data of steam turbine is processed with fuzzy and discrete method firstly, a multilayer BP neural network is structured secondly, the neural network is trained via teacher＇s guidance thirdly, and the neural network is optimized by genetic algorithm lastly. Based on the ontology of neural network, the data mining algorithm for classified fault diagnosis rules about steam turbine is brought forward, an experimental system for data mining and fault diagnosis of steam turbine based on neural network and genetic algorithm is implemented. Its diagnosis precision is 84%. And experiments do prove that it is feasible to use the method to develop a system for fault diagnosis of steam turbine, which is valuable for further study in more depth.