粗糙集用于建立基于多变量决策树的变压器故障诊断模型

采用决策树方法建立变压器故障诊断模型,可以方便地处理含有非数值特征的故障样本,且能够从样本中学习知识并简化知识,具有较好的适应性.但是采用单变量决策树描述复杂关系仍存在一定的局限性,本文提出基于多变量决策树的变压器故障诊断方法.并通过粗糙集辨识矩阵确定多变量检测特征选取,来实现多变量决策树的建立,能有效地约简知识,直观且易于理解.最后通过实例比较验证了方法的有效性.

Application of rough set to build transformer fault diagnosis model based on multivariate decision tree

Through decision tree to build the model of transformer fault diagnosis, not only fault samples with nonnumerical values can be processed, but also rules can be learned and knowledge can be simplified with better adaptive capability. However, there exists certain limitation to describe complex cause and reason relationships through univariate decision tree, in which any symptom may be detected repeatedly along one path. In this paper, transformer fault diagnosis approach based on multivariate decision tree is presented. To improve analytical efficiency, several symptoms can be detected synchronously in one node of the multivariate decision tree. Based on advantages of rough set, such as knowledge reduction and classification, through discernibility matrix of rough set to select symptoms and construct multivariate decision tree for transformer fault diagnosis, the diagnosis knowledge can be reduced. And the diagnosis model is visual and easy to understand. Results of comparison test verify the effectiveness of the approach.