A single Bayesian network classifier for monitoring with unknown classes. (October 2019)

• Record Type: Journal Article
• Authors: Atoui, M. Amine; Cohen, Achraf; Verron, Sylvain; Kobi, Abdessamad
• Is Part Of: Engineering applications of artificial intelligence. Volume 85(2019)
• Page Start: 681
• Page End: 690
• Publication Date: 2019-10
• Subjects: Bayesian networks -- Fault detection and diagnosis -- Classification -- Exclusion criteria; 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.2019.07.016
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: In this paper, the Conditional Gaussian Networks (CGNs), a form of Bayesian Networks (BN), are used as a statistical process monitoring approach to detect and diagnose faults. The proposed approach improves the structure of Bayesian networks and generalizes a few results regarding statistical tests and the use of an exclusion criterion. The proposed framework is evaluated using data from the benchmark Tennessee Eastman Process (TEP) with various scenarios.