基于Rough set知识获取的故障数据表聚类离散化方法研究

为了从故障诊断实例的数据资源中知识获取,对具有连续属性值的故障实例数据表转化为Rough set(RS)理论离散数据类型的决策表的正确映射进行了研究.将改进的k-means聚类算法用于故障实例数据表的离散映射方案设计.在设置故障实例的导师决策类别数为聚类数k对论域划分的基础上,提出了根据均值聚类中心排序序号构造离散映射符号集、相对均值聚类中心由相似测度确定连续属性值映射编码的离散化方案.实例表明,该方法反映了转子振动故障特征的一般规律,断点设置具有动态自适应和抗干扰特性.获得的决策规则可用于构造和扩充故障诊断知识库.

STUDY ON CLUSTERING DISCRETIZATION SCHEME TO FAULTS DATA TABLE IN KNOWLEDGE ACQUISITION BASED ON ROUGH SET THEORY

For the knowledge acquisition from the data table recorded faults cases with continuous-value features, the discretization-mapping scheme translating the table into the generalization information system of rough set theory is investigated. The direct k-means algorithm of the clustering analysis is improved. The amount of faults confirmed by experts in the table is set as the classification amount k and the set of indexes of mean clustering centers ordered by sort ascending is set as mapping function. On the basis of the partition universe with k, the discretization scheme to the table is proposed. The discrete symbol denoting a continuous value is the same as the symbol of the center provided with the nearest distance between the value and a group of clustering centers. By the process, a normal table accord with the pattern of rough set theory is acquired and a few decision-making rules are extracted. The rules show that the scheme has the performances optimizing the partition points and rejecting the outside fluctuation. They reveal the generalization characters of the faults and can be used to construct and to extend the knowledge database of the fault diagnosis devices of a rotor-bearings system.