基于模糊粗糙集和事例推理的凝汽器真空故障诊断

针对凝汽器真空故障诊断的不确定性和复杂性，提出一种基于模糊粗糙集和事例推理的凝汽器故障诊断新方法。首先，运用模糊粗糙集属性约简方法对故障特征进行约简和权重分配，不仅提取了反映故障的主要特征量，降低了特征变量之间的非线性相关性，而且避免了人的主观性对权重分配的影响。然后，在分析凝汽器真空故障特征的基础上，建立凝汽器真空故障树，以约简特征作为条件对故障树根节点进行归纳检索，有效地减少了候选事例的数量，再通过最近邻法检索故障树叶节点，对凝汽器真空故障进行智能定位。通过对汽轮机凝汽器历史故障特征数据集仿真，验证了该方法的有效性。

A new method for fault diagnosis of condenser vacuum based on fuzzy rough set and case-based reasoning

Due to complexity and uncertainty of condenser fault diagnosis,a new method for fault diagnosis of condenser vacuum based on fuzzy rough set and case-based reasoning was presented.By analyzing the characteristics of condenser vacuum fault diagnosis,a fault tree was built.Weight membership was used to retrieval radical nodes of fault tree,and leaf nodes were retrieved by nearest neighbor algorithm.Considering many fault features and their relativity,fuzzy rough set was used in feature reduction and weight allocation to extract main features,reduce non-linear relationship between features and avoid human’s subjectivity influence on weight allocation.Simulating of turbo condenser fault data proved that the method is effective.