A Bayesian network dealing with measurements and residuals for system monitoring. (April 2016)

• Record Type: Journal Article
• Authors: Atoui, Mohamed Amine; Verron, Sylvain; Kobi, Abdessamad
• Is Part Of: Transactions of the Institute of Measurement and Control. Volume 38:Number 4(2016)
• Page Start: 373
• Page End: 384
• Publication Date: 2016-04
• Subjects: CGN -- data-driven methods -- model-based methods -- classification -- incidence matrix -- Bayesian networks -- water heater system; Automatic control -- Periodicals; Measuring instruments -- Periodicals; Commande automatique -- Périodiques; Mesure -- Instruments -- Périodiques; 681.2
• DOI: 10.1177/0142331215581446
• Languages: English
• ISSNs: 0142-3312

Abstract

The purpose of this paper is to present an original method for system monitoring with Bayesian networks. Our proposal is to associate a data-driven method to another model-based under a common tool. The two methods are first modeled under a Bayesian network (conditional Gaussian network), and then combined to evaluate the system state. In the proposed framework the residuals and measures coexist under a probabilistic framework. This approach is tested on a simulation of a water heater process under some various circumstances and shows better results than the two methods used alone.