A dual attribute weighted decision fusion system for fault classification based on an extended analytic hierarchy process. (September 2022)

• Record Type: Journal Article
• Authors: He, Yuchen; Lou, Ruichong; Wang, Yun; Wang, Jun; Fang, Xinyun
• Is Part Of: Engineering applications of artificial intelligence. Volume 114(2022)
• Publication Date: 2022-09
• Subjects: Fault classification -- Decision fusion system -- Analytic hierarchy process -- Dual attributes; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2022.105066
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: As substantial parts of process monitoring, fault classification techniques have been widely utilized in modern industries. However, most methods can only perform well under specific condition, which indicates that it is always difficult to ensure the classification efficiency for complex industrial processes using only one method. In this paper a weighted decision fusion system is proposed where an extended analytic hierarchy process (EAHP) structure is designed to give a comprehensive explanation for the weights assigned to the original fault classification results. Firstly, the fault category information is embedded in the EAHP structure, which makes it possible to consider both fault-wise and classifier-wise information in the fault classification. Secondly, an overall priority (OP) matrix is proposed to provide full prior knowledge for all classifiers. Different from previous researches, traditional OP vectors are replaced by the new-designed OP matrix which can contain more information about fault category. Thirdly, another confidence matrix is carried out to update the classification results. Compared with previous state-of-art, the confidence matrix can be adjusted according to the specific original classification result Finally, the effectiveness of the proposed method is verified by a numerical example and the Tennessee Eastman process (TEP) where the proposed decision fusion system shows superior fault classification results in both experiments.