Application of Improved Rough Set Reduction Algorithm in On-line Fault Diagnosis of Chemical Equipment

Abstract

The application of improved rough set reduction algorithm in Chemical Equipment on-line fault diagnosis is discussed. The rough set association degree is applied to attribute association degree matrix, and the improved rough set reduction algorithm is obtained, and the application scope of this method in Chemical Equipment online fault diagnosis is studied. The results show that the improved rough set reduction algorithm is a new data mining method, which can effectively solve the problem of attribute classification of equipment in online fault diagnosis. Therefore, the improved rough set reduction algorithm can quickly and effectively diagnose on-line faults in Chemical Equipment system and eliminate the corresponding faults in time.