Advertisement

An Integrated Process for FDIR Design in Aerospace

  • Benjamin Bittner
  • Marco Bozzano
  • Alessandro Cimatti
  • Regis De Ferluc
  • Marco Gario
  • Andrea Guiotto
  • Yuri Yushtein
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8822)

Abstract

The correct operation of complex critical systems increasingly relies on the ability to detect and recover from faults. The design of Fault Detection, Isolation and Recovery (FDIR) sub-systems is highly challenging, due to the complexity of the underlying system, the number of faults to be considered and their dynamics. Existing industrial practices for FDIR are often based on ad-hoc solutions, that are conceived and developed late in the design process, and do not consider the software- and system-level RAMS analyses data (e.g., FTA and FMEA).

In this paper we propose the FAME process: a novel, model-based, integrated process for FDIR design, that addresses the shortcomings of existing practices. This process aims at enabling a consistent and timely FDIR conception, development, verification and validation. The process is supported by the FAME environment, a model-based toolset that encompasses a wide range of formal analyses, and supports the FDIR design by providing functionality to define mission and FDIR requirements, fault propagation modeling, and automated synthesis of FDIR models. The FAME process and environment have been developed within an ESA-funded study, and have been thoroughly evaluated by the industrial partners on a case study derived from the ExoMars project.

Keywords

Model Checker Fault Injection Spacecraft Attitude Failure Propagation Symbolic Model Check 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Abdelwahed, S., Karsai, G., Mahadevan, N., Ofsthun, S.C.: Practical implementation of diagnosis systems using timed failure propagation graph models. IEEE Transactions on Instrumentation and Measurement 58(2), 240–247 (2009)CrossRefGoogle Scholar
  2. 2.
    ADeS, a simulator for AADL., http://www.axlog.fr/aadl/ades_en.html
  3. 3.
    Bensana, E., Pucel, X., Seguin, C.: Improving FDIR of Spacecraft Systems with Advanced Tools and Concepts. In: Proc. ERTS (2014)Google Scholar
  4. 4.
    Berthomieu, B., Bodeveix, J.P., Farail, P., Filali, M., Garavel, H., Gaufillet, P., Lang, F., Vernadat, F., et al.: Fiacre: An Intermediate Language for Model Verification in the TOPCASED Environment. In: Proc. ERTS (2008)Google Scholar
  5. 5.
    Bittner, B., Bozzano, M., Cimatti, A., De Ferluc, R., Gario, M., Guiotto, A., Yushtein, Y.: An Integrated Process for FDIR Design in Aerospace. In: Ortmeier, F., Rauzy, A. (eds.) IMBSA 2014. LNCS, vol. 8822, pp. 82–95. Springer, Heidelberg (2014)Google Scholar
  6. 6.
    Blanquart, J.-P., Valadeau, P.: Model-based FDIR development and validation. In: Proc. MBSAW (2011)Google Scholar
  7. 7.
    Bozzano, M., Cimatti, A., Gario, M., Tonetta, S.: A formal framework for the specification, verification and synthesis of diagnosers. In: Workshops at the Twenty-Seventh AAAI Conference on Artificial Intelligence (2013)Google Scholar
  8. 8.
    Bozzano, M., Cimatti, A., Katoen, J.-P., Nguyen, V.Y., Noll, T., Roveri, M.: Safety, dependability, and performance analysis of extended AADL models. The Computer Journal (March 2010) doi: 10.1093/comGoogle Scholar
  9. 9.
    Bozzano, M., Cimatti, A., Nguyen, V.Y., Noll, T., Katoen, J.-P., Roveri, M.: Codesign of Dependable Systems: A Component-Based Modeling Language. In: Proc. MEMOCODE 2009 (2009)Google Scholar
  10. 10.
    Bozzano, M., Villafiorita, A.: The FSAP/NuSMV-SA Safety Analysis Platform. Software Tools for Technology Transfer 9(1), 5–24 (2007)CrossRefGoogle Scholar
  11. 11.
    Cimatti, A., Dorigatti, M., Tonetta, S.: OCRA: A tool for checking the refinement of temporal contracts. In: ASE, pp. 702–705 (2013)Google Scholar
  12. 12.
    Cimatti, A., Pecheur, C., Cavada, R.: Formal Verification of Diagnosability via Symbolic Model Checking. In: Proc. IJCAI, pp. 363–369. Morgan Kaufmann (2003)Google Scholar
  13. 13.
    Cimatti, A., Roveri, M., Bertoli, P.: Conformant planning via symbolic model checking and heuristic search. Artificial Intelligence 159(1), 127–206 (2004)MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
  15. 15.
    European Cooperation for Space Standardization. European cooperation for space standardization web site, http://www.ecss.nl/.
  16. 16.
  17. 17.
    Grunske, L., Kaiser, B., Papadopoulos, Y.: Model-driven safety evaluation with state-event-based component failure annotations. In: Heineman, G.T., Crnković, I., Schmidt, H.W., Stafford, J.A., Ren, X.-M., Wallnau, K. (eds.) CBSE 2005. LNCS, vol. 3489, pp. 33–48. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  18. 18.
    Güdemann, M., Ortmeier, F.: A Framework for Qualitative and Quantitative Formal Model-Based Safety Analysis. In: Proc. HASE, pp. 132–141 (2010)Google Scholar
  19. 19.
    Mokos, K., Meditskos, G., Katsaros, P., Bassiliades, N., Vasiliades, V.: Ontology-Based Model Driven Engineering for Safety Verification. In: Proc. SEAA, pp. 47–54. IEEE (2010)Google Scholar
  20. 20.
    The nuXmv model checker, https://nuxmv.fbk.eu
  21. 21.
    The XSAP safety analysis platform, https://es.fbk.eu/tools/xsap

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Benjamin Bittner
    • 1
  • Marco Bozzano
    • 1
  • Alessandro Cimatti
    • 1
  • Regis De Ferluc
    • 2
  • Marco Gario
    • 1
  • Andrea Guiotto
    • 3
  • Yuri Yushtein
    • 4
  1. 1.Fondazione Bruno KesslerTrentoItaly
  2. 2.Thales Alenia SpaceFrance
  3. 3.Thales Alenia SpaceItaly
  4. 4.European Space Agency (ESA), ESTECNoordwijkThe Netherlands

Personalised recommendations