Complex machinery like spacecraft, aircraft, or chemical plants are equipped with fault detection and diagnosis systems. Due to their safety-critical nature, such diagnosis systems have to undergo rigorous Verification and Validation (V&V). In this paper, we present a tool suite to facilitate V&V of the deployed diagnostic system. The V&V relies on the paradigms of cross validation (to compare the diagnosis results of the deployed reasoner against those of other, more advanced reasoners), automatic fault scenario generation (to support extensive testing and coverage analysis), and parametric model analysis (to enrich test sets and for robustness and sensitivity analysis). We present the application of this tool architecture towards the V&V of the diagnosis system based on the TEAMS tool suite towards a subsystem in the NASA cryogenic fuel loading facility.
How to Cite
verification and validation, TEAMS, cryogenic fuel, cross-validation, parametric model analysis
Abdelwahed, S., Karsai, G., & Biswas, G. (2005). A consistency-based robust diagnosis approach for temporal causal systems. In 16th International Workshop on Principles of Diagnosis (pp. 73–79).
Abdelwahed, S., Karsai, G., Mahadevan, N., & Ofsthun, S. C. (2009). Practical considerations in systems diagnosis using timed failure propagation graph models. Instru- mentation and Measurement, IEEE Transactions on, 58(2), 240–247.
Giannakopoulou, D., Bushnell, D., Schumann, J., Erzberger, H., & Here, K. (2011). Formal testing for separation assurance. Ann. Math. Artif. Intell., 63(1), 5–30.
Goodrich, C., Narasimhan, S., Daigle, M., Hatfield, W., & Johnson, R. (2007). Applying model-based diagnosis to a rapid propellant loading system.
Gundy-Burlet, K., Schumann, J., Menzies, T., & Barrett, T. (2008). Parametric Analysis of ANTARES Re-entry Guidance Algorithms using advanced Test Generation and Data Analysis. In Proc. i-SAIRAS 2008.
Hayden, S., Oza, N., Mah, R., Mackey, R., Narasimhan, S., Karsai, G., Shirley, M. (2006). Diagnostic technology evaluation report for on-board crew launch vehicle (Tech. Rep.). NASA.
Lindsey, A. E., & Pecheur, C. (2004). Simulation-based verification of autonomous controllers via Livingstone Pathfinder. In K. Jensen & A. Podelski (Eds.), Proceedings TACAS 2004 (Vol. 2988, pp. 357–371). Springer.
Luo, J., Tu, H., Pattipati, K., Qiao, L., & Chigusa, S. (2005). Graphical models for diagnosis knowledge representation and inference. In Autotestcon. IEEE (p. 483-489).
Mahadevan, N., & Karsai, G. (2000–2014). Fact tool suite. https://fact.isis.vanderbilt.edu/.
Narasimhan, S., & Brownston, L. (2007). HyDE – a general framework for stochastic and hybrid model-based diagnosis. In Proc. of 18th international workshop on principles of diagnosis (DX ’07) (pp. 162–169).
Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of plausible inference Morgan Kaufmann: .
Reed, E., Schumann, J., & Mengshoel, O. (2011). Verification and validation of system health management models using parametric testing. Proc. of Infotech@ Aerospace 2011.
RTCA. (2011). Do-178c: Software considerations in airborne systems and equipment certification. Retrieved from http://www.rtca.org
Schumann, J., Bajwa, A., Berg, P., & Thirumalainambi, R. (2010). Parametric testing of launch vehicle FDDR models. In AIAA Space.
Schumann, J., Gundy-Burlet, K., Pasareanu, C., Menzies, T., & Barrett, T. (2009). Software V&V support by parametric analysis of large software simulation systems. In Proc. IEEE Aerospace. IEEE Press.
Schumann, J., Srivastava, A., & Mengshoel, O. (2010). Who guards the guardians? — toward V&V of health management software. In RV 2010. Springer.
Schwabacher, M. A., Feather, M. S., & Markosian, L. Z. (2008). Verification and validation of advanced fault detection, isolation and recovery for a NASA space system. In Proc. PHM 2008.
Srivastava, A., & Schumann, J. (2013). Software health management: A necessity for safety critical systems. Innovations in Systems and SW Eng., 9(4), 219–233.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.