基于粗糙集和 SVM 理论的柴油机故障分类预测诊断

文中利用粗糙集和 SVM 理论相结合的方法对柴油机故障进行快速准确分类预测诊断。首先对收集的故障特征数据进行预处理，再运用粗糙集理论进行属性约简得到最优决策属性表，然后使用 SVM 理论中的分类预测规则对最优决策属性表进行诊断分类，得出诊断结果。通过实例分析验证了该诊断方法优于单一的粗糙集诊断和 SVM 诊断。

Rough sets and SVM theory based on the fault of diesel diagnosis classification prediction

In this paper,we combine rough set with the theory of SVM to achieve rapid accurate classification projections for diesel engine fault diagnosis. We first preprocesses the collected fault feature data,uses rough set theory to attribute reduction for getting the optimal decision attribute table and then uses the theory SVM classifi-cation prediction rules for diagnosis classification of optimal decision attribute table so as to conclude the diagno-sis. The example shows that this diagnostic method is better than single diagnosis of rough set and SVM.