A Method of the Rules Extraction for Fault Diagnosis Based on Rough Set Theory and Decision Network

Directing to the inconsistency of the fault diagnosis information, a method of the rules extraction for fault diagnosis based on rough set theory and decision network is proposed. The fault diagnosis decision system attributes are reduced through discernibility matrix and discernibility function firstly, and then a decision network with different reduced levels is constructed. Initialize the network´s node with the attribute reduction sets and extract the decision rule sets according to the node of the decision network. In addition, the coverage degree based on confidence degree was applied to filter noise and evaluate the extraction rules. The availability of this method is proved by a fault diagnosis example of rotating machines.