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A method to generate equiprobale runs in TFPG models

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Published:14 June 2016Publication History

ABSTRACT

FDIR functionalities play a key role in several fields and a lot of techniques and methods have been developed in order to make them possible. TFPG is a causal model that captures the temporal aspects of failure propagation in a wide variety of engineering systems. As other sources of information, they require to be stored and retrieved. A key to index and query a TFPG documental base is in the sequence of events, i.e. runs, they are able to model. In this paper we propose a method to produce equiprobable runs of the TFPG model that can be used as support to index generation.

References

  1. S. Abdelwahed and G. Karsai. Failure prognosis using timed failure propagation graphs. In The International Conference of the Prognostics and Health Management Society 2009, San Diego, CA 2009, 2009.Google ScholarGoogle Scholar
  2. Sherif Abdelwahed, Gabor Karsai, and Gautam Biswas. System diagnosis using hybrid failure propagation graphs. In The 15th International Workshop on Principles of Diagnosis. Citeseer, 2004.Google ScholarGoogle Scholar
  3. Marco Bozzano, Alessandro Cimatti, Marco Gario, and Andrea Micheli. Smt-based validation of timed failure propagation graphs. In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, AAAI'15, pages 3724--3730. AAAI Press, 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Elías F. Combarro, Ignacio F. Rúa, and José Ranilla. New advances in the computational exploration of semifields. Int. J. Comput. Math., 88(9):1990--2000, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Irene Díaz, Elías F. Combarro, Pasquale Marinaro, and Luigi Troiano. Ranking COMMPS chemical substances by an improved POT/RLE approach. Journal of Chemical Information and Modeling, 53(12):3190--3201, 2013.Google ScholarGoogle ScholarCross RefCross Ref
  6. A. Dubey, G. Karsai, and N. Mahadevan. Model-based software health management for real-time systems. In Aerospace Conference, 2011 IEEE, pages 1--18, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Sandra Hayden, Nikunj Oza, Robert Mah, Ryan Mackey, Sriram Narasimhan, Gabor Karsai, Scott Poll, Somnath Deb, and Mark Shirley. Diagnostic technology evaluation report for on-board crew launch vehicle. NASA, Tech. Rep, 2006.Google ScholarGoogle Scholar
  8. S. Ierace, P. Marinaro, P. Tatavitto, and L. Troiano. Profiling the power usage of industrial machinery by ann. In Soft Computing and Pattern Recognition (SoCPaR), 2010 International Conference of, pages 413--418, 2010.Google ScholarGoogle ScholarCross RefCross Ref
  9. S. Ierace, R. Pinto, L. Troiano, and S. Cavalieri. Neural network as an efficient diagnostics tool: A case study in a textile company. volume 1, pages 122--127, 2010.Google ScholarGoogle Scholar
  10. T. Kurtoglu, S.B. Johnson, E. Barszcz, J.R. Johnson, and P.I. Robinson. Integrating system health management into the early design of aerospace systems using functional fault analysis. In Prognostics and Health Management, 2008. PHM 2008. International Conference on, pages 1--11, 2008.Google ScholarGoogle ScholarCross RefCross Ref
  11. N. Mahadevan, S. Abdelwahed, A. Dubey, and G. Karsai. Distributed diagnosis of complex systems using timed failure propagation graph models. In AUTOTESTCON, 2010 IEEE, pages 1--6, 2010.Google ScholarGoogle Scholar
  12. Amit Misra. Senor-based diagnosis of dynamical systems. PhD thesis, Vanderbilt University, 1994. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. S.C. Ofsthun and S. Abdelwahed. Practical applications of timed failure propagation graphs for vehicle diagnosis. In Autotestcon, 2007 IEEE, pages 250--259, 2007.Google ScholarGoogle ScholarCross RefCross Ref
  14. S. Rampone, V. Pierro, L. Troiano, and I.M. Pinto. Neural network aided glitch-burst discrimination and glitch classification. International Journal of Modern Physics C, 24(11), 2013.Google ScholarGoogle ScholarCross RefCross Ref
  15. Jean-Marc Roussel, Alessandro Giua, et al. Designing dependable logic controllers using the supervisory control theory. In Proceedings of the 16th IFAC World Congress, 2005, 2005.Google ScholarGoogle ScholarCross RefCross Ref
  16. J. Schumann, O.J. Mengshoel, and T. Mbaya. Integrated software and sensor health management for small spacecraft. In Space Mission Challenges for Information Technology (SMC-IT), 2011 IEEE Fourth International Conference on, pages 77--84, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Claude E. Shannon. A mathematical theory of communication. The Bell System Technical Journal, 27:379--423, 623--656, July, October 1948.Google ScholarGoogle ScholarCross RefCross Ref
  18. L. Troiano, M. Tipaldi, A. Di Cerbo, M. Hoping, D. De Pasquale, and B. Bruenjcs. Satellite fdir practices using timed failure propagation graphs. volume 11, pages 8524--8531, 2012.Google ScholarGoogle Scholar
  19. Luigi Troiano and Giacomo Scibelli. A time-efficient breadth-first level-wise lattice-traversal algorithm to discover rare itemsets. Data Mining and Knowledge Discovery, pages 1--35, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Luigi Troiano, Giacomo Scibelli, and Cosimo Birtolo. A fast algorithm for mining rare itemsets. In ISDA, pages 1149--1155, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library

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  • Published in

    cover image ACM Other conferences
    CERI '16: Proceedings of the 4th Spanish Conference on Information Retrieval
    June 2016
    146 pages
    ISBN:9781450341417
    DOI:10.1145/2934732

    Copyright © 2016 ACM

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    Publication History

    • Published: 14 June 2016

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    CERI '16 Paper Acceptance Rate18of27submissions,67%Overall Acceptance Rate36of51submissions,71%
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