Publicerad: 2013-03-27
ISBN: 978-91-7519-621-3 (print)
ISSN: 1650-3686 (tryckt), 1650-3740 (online)
Diagnosis of complex systems is a critical area for most real-world systems. Given the wide range of system types; including physical systems; logic circuits; state-machines; control systems; and software; there is no commonlyaccepted modeling language or inference algorithms for model-Based Diagnosis (MBD) of such systems. Designing a language that can be used for modeling such a wide class of systems; while being able to efficiently solve the model; is a formidable task. The computational efficiency with which a given model can be solved; although often neglected by designers of modeling languages; is a key to parameter identification and answering MBD challenges. We address this freedom-of-modeling versus model-solving efficiency trade-off challenge by evolving a language for MBD of physical system; called LYDIA. In this paper we report on the abilities of LYDIA to model a class of physical systems; the algorithms that we use for solving MBD problems and the results that we have obtained for several challenging systems.
model-based diagnosis; model-based testing; automated reasoning; modeling language
[1] Shai A Arogeti; DanweiWang; and Chang Boon Low. Mode identification of hybrid systems in the presence of fault. Industrial Electronics; IEEE Transactions on; 57(4):1452– 1467; 2010.
[2] Olof Bäck. Modelling for diagnosis in Modelica: implementation and analysis. PhD thesis; University of Linköping; 2008.
[3] Franc Brglez and Hideo Fujiwara. A neutral netlist of 10 combinational benchmark circuits and a target translator in fortran. In Proc. ISCAS’85; pages 695–698; 1985.
[4] Peter Bunus; Olle Isaksson; Beate Frey; and Burkhard Münker. Model-based diagnostics techniques for avionics applications with rodon. In 2nd Workshop on Aviation System Technology. Citeseer; 2009.
[5] Peter Bunus; Olle Isaksson; Beate Frey; and Burkhard Münker. Rodon-a model-based diagnosis approach for the dx diagnostic competition. Proc. DX’09; pages 423–430; 2009.
[6] Christos G Cassandras and Stephane Lafortune. Introduction to discrete event systems; volume 11. Kluwer academic publishers; 1999.
[7] François E Cellier and Àngela Nebot. The modelica bond graph library. In 4th International Modelica Conference; 2005.
[8] Adnan Darwiche and Pierre Marquis. A knowlege compilation map. Journal of Artificial Intelligence Research; 17:229–264; 2002.
[9] Nando de Freitas. Rao-blackwellised particle filtering for fault diagnosis. In Proc. AEROCONF’02; volume 4; pages 1767–1772; 2002.
[10] Johan de Kleer; Alan Mackworth; and Raymond Reiter. Characterizing diagnoses and systems. Artificial Intelligence; 56(2-3):197–222; 1992.
[11] Alexander Feldman; Gregory Provan; and Arjan van Gemund. Approximate model-based diagnosis using greedy stochastic search. Journal of Artificial Intelligence Research; 38:371–413; 2010.
[12] Peter Fritzson and Vadim Engelson. Modelica—a unified object-oriented language for system modeling and simulation. ECOOP’98—Object-Oriented Programming; pages 67–90; 1998.
[13] Gregor Gössler and Joseph Sifakis. Composition for component-based modeling. Science of Computer Programming; 55(1):161–183; 2005.
[14] I. Graham; A. O’Callaghan; and A.C.Wills. Object-Oriented Methods: Principles & Practice. Addison-Wesley Object Technology Series. Addison-Wesley; 2000.
[15] Thomas A Henzinger. The theory of hybrid automata. In Logic in Computer Science; 1996. LICS’96. Proceedings.; Eleventh Annual IEEE Symposium on; pages 278–292. IEEE; 1996.
[16] Ron M. Kielkowski. Inside SPICE. Electronic packaging and interconnection series. McGraw-Hill; 1998.
[17] Uri Lerner; Ronald Parr; Daphne Koleer; and Gautam Biswas. Bayesian fault detection and diagnosis in dynamic systems. In Proc. AAAI’00; pages 531–537; 2000.
[18] Pieter J Mosterman and Gautam Biswas. A comprehensive methodology for building hybrid models of physical systems. Artificial Intelligence; 121(1):171–209; 2000.
[19] Behrooz Parhami. Computer Arithmetic: Algorithms and Hardware Designs. Oxford University Press; Inc.; New York; NY; USA; 2nd edition; 2009.
[20] Raymond Reiter. A theory of diagnosis from first principles. Artificial intelligence; 32(1):57–95; 1987.
[21] AK Samantaray; K. Medjaher; B. Ould Bouamama; M. Staroswiecki; and G. Dauphin-Tanguy. Diagnostic bond graphs for online fault detection and isolation. Simulation Modelling Practice and Theory; 14(3):237–262; 2006.
[22] Morris Tenenbaum and Harry Pollard. Ordinary Differential Equations: An Elementary Textbook for Students of Mathematics; Engineering; and the Sciences. Dover Books on Mathematics. Dover Publications; 1963.
[23] D.G. Zill. Differential Equations With Computer Lab Experiments. Brooks/Cole; 1998.
