基于模糊粗糙集数据挖掘的汽轮机组故障诊断研究

针对当前专家系统知识获取瓶颈的难题,提出了基于模糊粗糙集数据挖掘的汽轮机组故障诊断方法。模糊粗糙集理论把知识直接与真实或抽象世界有关的不同模式联系在一起,能有效分析处理不精确、不完整等各种不完备信息,并从中发现隐含的知识,揭示潜在的规律。将汽轮机组故障历史数据进行模糊化及离散化处理,构建故障诊断决策表,以决策表作为主要工具,即"知识库",采用模糊粗糙集数据挖掘方法直接从决策表中提取出潜在的诊断规则,为汽轮机组提供有效的故障诊断。提出了基于模糊粗糙集的分类规则学习和约简算法,实现了基于模糊粗糙集数据挖掘的汽轮机组故障诊断系统,其诊断正确率达到了88%。实验表明该方法可行,对汽轮机组故障诊断系统的设计具有借鉴意义和深入研究的价值

A Novel Approach for Fault Diagnosis of Steam Turbine Unit Based on Fuzzy Rough Set Data Mining Theory

A novel approach for fault diagnosis of steam turbine unit based on fuzzy rough set(RS) data mining theory is brought forward,aimed at overcoming shortages of some current knowledge attaining methods.The historical fault data of steam turbine unit is processed with fuzzy and scatter method.The processed data is used to structure the fault diagnosis decision-making table which is treated as "knowledge database".This paper introduced rough sets data mining method to take potential diagnosis rule from the fault diagnosis decision-making table of steam turbine unit.These rules can offer effective fault diagnosis service for steam turbine unit.The algorithm for classified rule learning and reducing is brought forward,and an experimental system for fault diagnosis of steam turbine unit based on fuzzy rough set data mining theory is implemented.Their diagnosising precision is above 88%.And experiments do prove that it is feasible to use the method to develop a system for fault diagnosis of steam turbine unit,which is valuable for further study in more depth.