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Model-Based Diagnosis of Hybrid Systems Using Satisfiability Modulo Theory

 
: Diedrich, Alexander; Maier, Alexander; Niggemann, Oliver

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Association for the Advancement of Artificial Intelligence -AAAI-:
The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019. The Thirty-First Innovative Applications of Artificial Intelligence Conference, IAAI 2019. The Ninth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019 : Honolulu, Hawaii, USA, January 27 - February 1, 2019
Menlo Park: AAAI Press, 2019
ISBN: 978-1-57735-809-1
S.1452-1459
Conference on Artificial Intelligence (AAAI) <33, 2019, Honolulu/Hawaii>
Innovative Applications of Artificial Intelligence Conference (IAAI) <31, 2019, Honolulu/Hawaii>
Symposium on Educational Advances in Artificial Intelligence (EAAI) <9, 2019, Honolulu/Hawaii>
Englisch
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()

Abstract
Currently, detecting and isolating faults in hybrid systems is often done manually with the help of human operators. In this paper we present a novel model-based diagnosis approach for automatically diagnosing hybrid systems. The approach has two parts: First, modelling dynamic system behaviour is done through well-known state space models using differential equations. Second, from the state space models we calculate Boolean residuals through an observer-pattern. The novelty lies in implementing the observer pattern through the use of a symbolic system description specified in satisfiability theory modulo linear arithmetic. With this, we create a static situation for the diagnosis algorithm and decouple modelling and diagnosis. Evaluating the system description generates one Boolean residual for each component. These residuals constitute the fault symptoms. To find the minimum cardinality diagnosis from these symptoms we employ Reiter’s diagnosis lattice.
For the experimental evaluation we use a simulation of the Tennessee Eastman process and a simulation of a four-tank model. We show that the presented approach is able to identify all injected faults.

: http://publica.fraunhofer.de/dokumente/N-565569.html