This paper extends model-Based diagnosis (MBD) (Reiter, 1987; de Kleer and Williams, 1987) to systems which convert, move and process materials or objects. Examples of such systems are printers, refineries, manufacturing lines and food processing plants. Such plants present two challenges to model-based diagnosis: (1) the plant may process with very high speed while handling multiple objects in parallel such that retaining full details of behavior of all past objects is impractical, and (2) complex multi-way interactions can occur among components operating on the same object. We address the first challenge by synopsizing past behavior and the current knowledge in a data structure of linear size in the number of components in the system. The second challenge is addressed by introducing the notion of interaction fault. An interaction fault is present if a set of components operating on the same object, damage the object even though each component alone produces non noticeable damage. Introducing interaction faults is much simpler than introducing fine-grained models of component-object interactions. We demonstrate the approach on a highly redundant printer
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artificial intelligence, diagnosis, diagnostic algorithm, diagnostic performance, fault adaptive controls, fault detection, fault diagnosis, Fault tolerant control, model based diagnostics, model-based methods
(Darwiche, 2001) Adnan Darwiche. Decomposable negation normal form. Journal of the ACM, 48(4):608–647, 2001.
(Davis, 1984) R. Davis. Diagnostic reasoning based on structure and behavior. Artificial Intelligence, 24(1):347–410, 1984.
(de Kleer and Williams, 1987) J. de Kleer and B. C. Williams. Diagnosing multiple faults. aij, 32(1):97–130, April 1987. Also in: Readings in NonMonotonic Reasoning, edited by Matthew L. Ginsberg, (Morgan Kaufmann, 1987), 280–297.
(deKleeretal.,1992) J.deKleer,A.Mackworth,and R. Reiter. Characterizing diagnoses and systems. Artificial Intelligence, 56(2-3):197–222, 1992.
(de Kleer, 2007a) J. de Kleer. Diagnosing intermit- tent faults. In 18th International Workshop on Prin- ciples of Diagnosis, pages 45–51, Nashville, USA, 2007.
(de Kleer, 2007b) J. de Kleer. Modeling when con- nections are the problem. In Proc 20th IJCAI, pages 311–317, Hyderabad, India, 2007.
(Dvorak and Kuipers, 1989) D. Dvorak and B. Kuipers. Model-based monitoring of dy- namic systems. In Proc. 11th IJCAI, pages 1238–1243, Detroit, 1989.
(Eiter and Gottlob, 1995) Thomas Eiter and Georg Gottlob. The complexity of logic-based abduction. J. ACM, 42(1):3–42, 1995.
(Fromherz et al., 2003) M.P.J. Fromherz, D.G. Bo- brow, and J. de Kleer. Model-based computing for design and control of reconfigurable systems. The AI Magazine, 24(4):120–130, 2003.
(Grastien et al., 2007) Alban Grastien, Anbulagan, Jussi Rintanen, and Elena Kelareva. Diagnosis of discrete-event systems using satisfiability algo- rithms. In AAAI, pages 305–310, 2007.
(Koren and Kohavi, 1977) Israel Koren and Zvi Ko- havi. Diagnosis of intermittent faults in com- binational networks. IEEE Trans. Computers, 26(11):1154–1158, 1977.
(Kuhn et al., 2008) Lukas Kuhn, Bob Price, Johan de Kleer, Minh Do, and Rong Zhou. Pervasive diagno- sis: Integration of active diagnosis into production plans. In proceedings of AAAI, Chicago, Illinois, USA, 2008.
(Muscettola et al., 1998) Nicola Muscettola, P. Pan- durang Nayak, Barney Pell, and Brian C. Williams. Remote agent: To boldly go where no AI system has gone before. Artificial Intelligence, 103(1-2):5–47, 1998.
(Reiter, 1987) R. Reiter. A theory of diagnosis from first principles. Artificial Intelligence, 32(1):57–96, 1987.
(Shearer et al., 1971) J. L. Shearer, A. T. Murphy, and H. H. Richardson. Introduction to System Dynam- ics. Addison Wesley, Reading, MA, 1971.
(Williams and Nayak, 1996) B. C. Williams and P. P. Nayak. A model-based approach to reactive self- configuring systems. In Proceedings of the National Conference on Artificial Intelligence (AAAI96), pages 971–978, 1996.
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