Cloud-Rough Model Reduction with Application to Fault Diagnosis System

During the system fault period, usually the explosive growth signals including fuzziness and randomness are too redundant to make right decision for the dispatcher. So intelligent methods must be developed to aid users in maintaining and using this abundance of information effectively. An important issue in fault diagnosis system (FDS) is to allow the discovered knowledge to be as close as possible to natural languages to satisfy user needs with tractability, and to offer FDS robustness. At this junction, the cloud theory is introduced. The mathematical description of cloud has effectively integrated the fuzziness and randomness of linguistic terms in a unified way. A cloud-rough model is put forward. Based on it, a method of knowledge representation in FDS is developed which bridges the gap between quantitative knowledge and qualitative knowledge. In relation to classical rough set, the cloud-rough model can deal with the uncertainty of the attribute and make a soft discretization for continuous ones. A novel approach, including discretization, attribute reduction, value reduction and data complement, is presented. The data redundancy is greatly reduced based on an integrated use of cloud theory and rough set theory. Illustrated with a power distribution FDS shows the effectiveness and practicality of the proposed approach.