A qualitative event-based approach to multiple fault diagnosis in continuous systems using structural model decomposition. (August 2016)

• Record Type: Journal Article
• Authors: Daigle, Matthew J.; Bregon, Anibal; Koutsoukos, Xenofon; Biswas, Gautam; Pulido, Belarmino
• Is Part Of: Engineering applications of artificial intelligence. Volume 53(2016:May)
• Page Start: 190
• Page End: 206
• Publication Date: 2016-08
• Subjects: Fault diagnosis -- Model-based diagnosis -- Multiple faults -- Diagnosability -- Structural model decomposition -- Discrete-event systems; 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.2016.04.002
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Multiple fault diagnosis is a difficult problem for dynamic systems, and, as a result, most multiple fault diagnosis approaches are restricted to static systems, and most dynamic system diagnosis approaches make the single fault assumption. Within the framework of consistency-based diagnosis, the challenge is to generate conflicts from dynamic signals. For multiple faults, this becomes difficult due to the possibility of fault masking and different relative times of fault occurrence, resulting in many different ways that any given combination of faults can manifest in the observations. In order to address these challenges, we develop a novel multiple fault diagnosis framework for continuous dynamic systems. We construct a qualitative event-based framework, in which discrete qualitative symbols are generated from residual signals. Within this framework, we formulate an online diagnosis approach and establish definitions of multiple fault diagnosability. Residual generators are constructed based on structural model decomposition, which, as we demonstrate, has the effect of reducing the impact of fault masking by decoupling faults from residuals, thus improving diagnosability and fault isolation performance. Through simulation-based multiple fault diagnosis experiments, we demonstrate and validate the concepts developed here, using a multi-tank system as a case study.