Distributed Consistency-Based Diagnosis without Behavior

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Published Oct 11, 2010
DOI https://doi.org/10.36001/phmconf.2010.v2i1.1950
Gianfranco Lamperti
Dipartimento di Ingegneria dell’Informazione, Brescia, 25123, Italy
Marina Zanella
Dipartimento di Ingegneria dell’Informazione, Brescia, 25123, Italy

Abstract

This paper faces the task of distributed diagnosis without exploiting any component behavioral model. A diagnosis problem is specified by an observation that just states whether each system output is either correct or incorrect. The system is split into parts, and a distinct diagnoser, which is supplied with knowledge that has been compiled off-line and is capable of communicating with its neighbors, is assigned to each of them. A family of methods is proposed to compute local and global minimal diagnoses that are consistent with both the observation and the system description, the latter being a kind of control flow structure.

How to Cite

Lamperti , G. ., & Zanella, . M. . (2010). Distributed Consistency-Based Diagnosis without Behavior. Annual Conference of the PHM Society, 2(2). https://doi.org/10.36001/phmconf.2010.v2i1.1950
Abstract 219 | PDF Downloads 203

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Keywords

consistency-based diagnosis, distributed diagnosis

References
(Armant et al., 2008) V. Armant, P. Dague, and L. Si- mon. Distributed consistency-based diagnosis without conflicts. In 19th International Workshop on Principles of Diagnosis –DX’08, Blue Mountains, AU, 2008.
(Borrego et al., 2009) D. Borrego, R.M. Gasca, M.T. Go ́mez Lo ́pez, and I. Barba. Choreography analysis for diagnosing faulty activities in business- to-business collaboration. In 20th International Workshop on Principles of Diagnosis – DX’09, pages 171–178, Stockholm, S, 2009.
(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. Artificial Intelligence, 32(1):97–130, 1987.
(Fowler, 2004) M. Fowler. UML Distilled: a brief guide to the standard object modeling language. Addison-Wesley, 2004.
(Reiter, 1987) R. Reiter. A theory of diagnosis from first principles. Artificial Intelligence, 32(1):57–95, 1987.
Issue
Vol. 2 No. 2 (2010): Proceedings of the 21st International Workshop on the Principles of Diagnosis, held jointly with the Annual Conference of the PHM Society 2010
Section
Technical Research Papers

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