基于主元分析及粗糙集的多变量决策树构造方法

为解决故障诊断中单一方法难于处理大规模、多变量数据信息的问题,提出了一种利用主元分析方法和粗糙集理论相结合的多变量决策树构造方法.该方法利用主元分析对历史数据进行降维、去噪处理,得到由主元变量组成的决策信息.通过粗糙集理论中核属性和相对泛化的概念对此决策信息进行属性选择和样本集划分,构造出多变量决策树,并建立诊断规则知识库.基于汽轮机发电机组的轴系振动故障分析的实例验证了此方法的正确性,与其他方法相比较具有规模小、诊断规则易于提取的特点.

Multi-variable decision tree construction method based on principal component analysis and rough set

In order to solve the problem that a single method is difficult to deal with large scale,multi-variable data in fault diagnosis,a multi-variable decision tree construction method combining principal component analysis with rough set theory is proposed.Firstly,the method uses principal component analysis to make dimension reduction and remove noises for the historical data and attempts to get the decision-making information that consists of principal component variables.Secondly,the method presents attribute selection and sample set measure for the decision-making information by nuclear properties and relative generalization concept in the rough set theory to construct multi-variable decision tree,on this basis,establishes diagnosis rules repository.Finally,by use of a shafting vibration fault analysis example on steam turbine generator units,the validity is demonstrated.Compared with other methods,this method has the advantages of small scale and is easy to extract diagnosis rules.