An Improved Algorithm of Extracting Fault Diagnosis Rules Based on Rough Sets

Juanli Li, Zhaojian Yang*
College of Mechanical Engineering, Taiyuan University of Technology, Taiyuan, 030024, China

© 2014 Li and Yang

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: ( This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Rough set is a new data analysis theory. It is often used to deal with fuzzy and uncertain problems. Attribution reduction is the key step in obtaining the knowledge by utilizing rough set. An improved heuristic reduction algorithm of attribute significance is proposed in the study based on analyzing the classic knowledge acquisition method of rough set theory. The algorithm corrects the discernibility matrix and redefines the calculation method of attribute importance. Then it fuses the both, gets the core by using the revised method of discernibility matrix and calculates the attribute importance by using the weighted method and then the algorithm is applied to extract the rules of the hoist fault diagnosis. Verified by the experiment, using the algorithm, it can excavate high reliability diagnosis rules from existing history diagnosis knowledge and expert knowledge. This method can provide reasonable basis for fault diagnosis.

Keywords: Attribute importance, discernibility matrix, fault diagnosis, rules extraction, rough set.