智能故障诊断的粗糙决策模型

Intelligent Fault Diagnosis Model Based on Rough Sets and Decision Tree Theory

为了提高故障诊断的精度和降低误报率,提出了粗糙决策智能故障诊断模型·该模型可以对决策表进行无教师的规则提取;通过自学习,用较少的样本即可对故障进行分类·将复杂系统的原始样本集转化成了决策表,利用粗糙集具有较强的处理不确定和不完备信息的能力,对原始样本集的条件属性进行了约简处理;同时,利用决策树具有快速学习及分类的优势对约简后的决策表进行规则提取,提高了故障诊断的鲁棒性·给出了基于该模型的故障诊断步骤·以实例介绍了利用该模型进行故障诊断的全过程·

Rough sets and decision tree theory are introduced in complicated intelligent fault diagnosis system(CIFDS). A rough-decision fault diagnosis model is thus developed to ensure diagnosis precision and speed up the implementation of CIFDS. The model can extract rules directly from reduced decision table. Rough sets theory as a new mathematical tool is used to deal with inexact and uncertain knowledge for pattern recognition. The target is mainly to remove redundant information and seek for reduced decision tables. As a quickly learning theory and classification tool, decision tree is used to extract rules directly from reduced decision table so as to acquire satisfactory result. An example is given to show how to apply the intelligent fault diagnosis to RH-KTB vacuum metallurgical system. The effectiveness of the algorithm is therefore proved through the exemplification.