数据挖掘技术在挖掘机故障诊断中的应用

Application of data mining technique in excavator fault diagnosis

针对工程机械的特点,提出了将数据挖掘技术应用于挖掘机故障诊断中,利用粗糙集具有较强的处理不确定和不完备信息的能力,对决策表的条件属性进行约简处理;再利用C4.5决策树算法的高效性对约简后的决策表进行诊断规则提取;将产生的规则运用于挖掘机故障诊断中以实现快速故障诊断。最后,以实例介绍了利用该模型进行故障诊断的完整过程,可以看出该方案提高了挖掘机故障诊断的效率。

In view of the characteristics of engineering machinery, data mining technique fs applied to excavator fault diagnosis in this paper. The theory of rough sets as a new mathematical tool is strong at dealing with incomplete and uncertain information and used to discretize and reduce the initial sample sets; and as a quickly learning theory and classification tool, the C4.5 decision tree is used to extract diagnosis rules directly from reduced decision table; the rules generated are applied to excavator fault diagnosis for rapid fault diagnosis. Finally, an example is given to show the whole fault diagnosis process of excavator fault diagnosis system by use of the new model. As can be seen from this example, the method improves the efficiency of excavator fault diagnosis.