基于粗糙集理论的状态监测与故障诊断

为了提高监测诊断的效率和自动化、智能化水平,将粗糙集理论引入到复杂装备系统状态监测与故障诊断领域.运用基于粗糙集理论的监测参数与故障特征约简算法,对发动机监测诊断过程中大量的冗余特征进行压缩或约简,实现发动机技术状态监测参数的优选;并针对故障点建立决策表,利用粗糙集约简所获得的诊断规则进行智能故障诊断.实例表明:粗糙集监测诊断方法不仅大大减少了特征信息提取的工作量,也为在故障诊断中实现自主式学习和决策提供了很大的便利.

Condition Monitoring and Fault Diagnosis Based on Rough Set Theory

In order to raise the efficiency,automatization and intelligentization of condition monitoring and fault diagnosis for complex equipment systems,rough set theory is used to the field. A feature reduction algorithm based on rough set theory is adopted to extract condition information in monitoring and diagnosis for an engine,so that the technology condition monitoring parameters are optimized. The decision tables for each fault source are built and the diagnosis rules rooting in rough set reduction is applied to carry through intelligent fault diagnosis. The cases studied show that rough set method in condition monitoring and fault diagnosis can lighten the work burden in feature selection and afford advantages for autonomic learning and decision during diagnosis.