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An integrated multidomain functional failure and propagation analysis approach for safe system design

Published online by Cambridge University Press:  24 April 2013

Chetan Mutha ,
David Jensen ,
Irem Tumer  and
Carol Smidts
Chetan Mutha*
Affiliation:
Department of Mechanical and Aerospace Engineering, Ohio State University, Columbus, Ohio, USA
David Jensen
Affiliation:
Department of Mechanical Engineering, University of Arkansas, Fayetteville, Arkansas, USA
Irem Tumer
Affiliation:
School of Mechanical, Industrial and Manufacturing Engineering, Oregon State University, Corvallis, Oregon, USA
Carol Smidts
Affiliation:
Department of Mechanical and Aerospace Engineering, Ohio State University, Columbus, Ohio, USA
*
Reprint requests to: Chetan Mutha, 201 West 19th Avenue, W382 Scott Laboratory, Ohio State University, Columbus, OH. E-mail: [email protected]
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Abstract

Early system design analysis and fault removal is an important step in the iterative design process to avoid costly repairs in the later stages of system development. System complexity is increasing with increased use of software to control the physical system. There is a dearth of techniques to evaluate inconsistencies, incompatibility, and fault proneness of the system design in an integrated manner. The early design analysis technique presented in this paper aids a designer to understand the interplay between the multifaceted components and evaluate his/her design in an integrated manner. The technique allows simultaneous propagation of different types of faults from various domains and evaluates their functional impact over a period of time. The structure of the technique is explained using domain-specific conceptual metamodels, whereas the execution is based on the event sequence diagram, which is one of the established reliability and safety analysis techniques. One of the notable features of the proposed technique is the object-oriented nature of the system design representation. The technique is demonstrated with the help of a case study, and the execution results of two scenarios are evaluated to demonstrate the analysis capability of the proposed technique.

Keywords

Decision SupportEarly Design RationaleFault PropagationIntegrated DesignIntegrated Functional Analysis
Type
Regular Articles
Information
AI EDAM , Volume 27 , Issue 4 , November 2013 , pp. 317 - 347
Copyright
Copyright © Cambridge University Press 2013 

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References

REFERENCES

Baresi, L., & Pezzè, M. (2001). On formalizing UML with high-level petri nets. In Concurrent Object-Oriented Programming and Petri Nets (Agha, G.A., Cindio, F., & Rozenberg, G., Eds.), pp. 276–304. Berlin: Springer–Verlag.CrossRefGoogle Scholar
Berenji, H.R., Ametha, J., & Vengerov, D. (2003). Inductive learning for fault diagnosis. Fuzzy Systems 1, 726–731.CrossRefGoogle Scholar
Bracewell, R., & Sharpe, J. (1996). A functional descriptions used in computer support for qualitative scheme generation-“Schemebuilder.” Artificial Intelligence for Engineering Design, Analysis and Manufacturing 10(4), 333–345.CrossRefGoogle Scholar
Brown, D.C. (2007). AIEDAM at 20. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 21(1), 1–2.CrossRefGoogle Scholar
Catalyurec, U., Rutt, B., Metzroth, K., Hakobyan, A., Aldemir, T., Denning, R., Dunagun, S., & Kunsman, R. (2010). Development of a code-agnostic computational infrastructure for the dynamic generation of accident progression event trees. Reliability Engineering and System Safety 95(3), 278–294.CrossRefGoogle Scholar
Dasarathy, B. (1985). Timing constraints of real-time systems: constructs for expressing them, methods of validating them. IEEE Transactions on Software Engineering 11(1), 80–86.CrossRefGoogle Scholar
Deb, S., Pattipati, K.R., Raghavan, V., Shakeri, M., & Shrestha, R. (2002). Multi-signal flow graphs: a novel approach for system testability analysis and fault diagnosis. IEEE Aerospace and Electronic Systems Magazine 10(5), 14–25.CrossRefGoogle Scholar
Deng, Y. (2002). Function and behavior representation in conceptual mechanical design. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 16(5), 343–362.CrossRefGoogle Scholar
De Kleer, J., & Brown, J.S. (1984). A qualitative physics based on confluences. Artificial Intelligence 24(1), 7–83.CrossRefGoogle Scholar
Department of Defense. (1980). Military Standard: Procedures for Performing a Failure Mode, Effects, and Criticality Analysis (MIL-STD-1629A). Washington, DC: Department of Defense.Google Scholar
Devooght, J., & Smidts, C. (1992). Probabilistic reactor dynamics. I: The theory of continuous event trees. Nuclear Science and Engineering 111(3), 229–240.CrossRefGoogle Scholar
Erikson, H.-E., Penker, M., Lyons, B., & Fado, D. (2004). UML 2 Toolkit. Indianapolis, IN: Wiley.Google Scholar
FAA. (2000). FAA System Safety Handbook. Washington, DC: FAA.Google Scholar
Giarratano, J., & Riley, G. (1989). Expert Systems: Principles and Programming, p. 856. Boston: PWS-Kent.Google Scholar
Goseva-Popstojanova, K., Hassan, A., Guedem, A., Abdelmoez, W., Nassar, D.E.M., Ammar, H., & Mili, A. (2003). Architectural-level risk analysis using UML. IEEE Transactions on Software Engineering 29(10), 946–960.CrossRefGoogle Scholar
Grunske, L., & Han, J. (2008). A comparative study into architecture-based safety evaluation methodologies using AADL's error annex and failure propagation models. Proc. IEEE High Assurance Systems Engineering Symp., pp. 283–292, Nanking.Google Scholar
Hawkins, P.G., & Woollons, D.J. (1998). Failure modes and effects analysis of complex engineering systems using functional models. Artificial Intelligence in Engineering 12(4), 375–397.CrossRefGoogle Scholar
Hirtz, J., Stone, R.B., Mcadams, D.A., Szykman, S., & Wood, K.L. (2002). A functional basis for engineering design: reconciling and evolving previous efforts. Research in Engineering Design 13(2), 65–82.CrossRefGoogle Scholar
Huang, Z., & Jin, Y. (2008). Conceptual stress and conceptual strength for functional design-for-reliability. Proc. 20th Int. Conf. Design Theory and Methodology 2nd Int. Conf. Micro and Nanosystems, Vol. 4, pp. 437–447. New York: American Society of Mechanical Engineers.Google Scholar
Hutcheson, R.S., McAdams, D.A., & Stone, R.B. (2006). A function-based methodology for analyzing critical events. Proc. Int. Design Engineering Technical Conf. Computers and Information in Engineering Conf., Philadelphia, PA.Google Scholar
Iwu, F., & Toyn, I. (2003). Modeling and analyzing fault propagation in safety-related systems. Proc. Software Engineering Workshop 28th Annual NASA Goddard, pp. 167–174, Greenbelt, MD.CrossRefGoogle Scholar
Jensen, D.C., Tumer, I.Y., & Kurtoglu, T. (2008). Modeling the propagation of failures in software-driven hardware systems to enable risk-informed design. Proc ASME'08 Int. Mechanical Engineering Congr. Exposition (IMECE2008), Vol. 16, ppp. 283–293. New York: American Society of Mechanical Engineers.Google Scholar
Jensen, D.C., Tumer, I.Y., & Kurtoglu, T. (2009). Flow state logic (FSL) for analysis of failure propagation in early design. Proc. Int. ASME'09 Int. Design Engineering Technical Conf. Computers and Information in Engineering Conf. (Paper No. IDETC/CIE2009), Vol. 8, pp. 1033–1043. New York: American Society of Mechanical Engineers.Google Scholar
Johannessen, P., Grante, C., Alminger, A., Eklund, U., Torin, J., & Assessment, F.H. (2001). Hazard analysis in object oriented design of dependable systems. Proc. Dependable Systems and Networks, pp. 507–512, Göteborg, June 30–July 4.CrossRefGoogle Scholar
Kapadia, R. (2003). SymCure: a model-based approach for fault management with causal directed graphs. Developments in Applied Artificial Intelligence 2718, 582–591.CrossRefGoogle Scholar
Krus, D., & Grantham Lough, K. (2009). Function-based failure propagation for conceptual design. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 23(4), 409–426.CrossRefGoogle Scholar
Kurtoglu, T., & Tumer, I.Y. (2008). A graph-based fault identification and propagation framework for functional design of complex systems. Journal of Mechanical Design 30(5), 051401.Google Scholar
Kurtoglu, T., Tumer, I.Y., & Jensen, D.C. (2010). A functional failure reasoning methodology for evaluation of conceptual system architectures. Research in Engineering Design 21(4), 209–234.CrossRefGoogle Scholar
Labeau, P.E., Smidts, C., & Swaminathan, S. (2000). Dynamic reliability: towards an integrated platform for probabilistic risk assessment. Reliability Engineering & System Safety 68(3), 219–254.CrossRefGoogle Scholar
Lapp, S.A., & Powers, G.J. (1977). Computer-aided synthesis of fault-trees. IEEE Transactions on Reliability 26(1), 2–13.CrossRefGoogle Scholar
Lee, W.S., Grosh, D.L., Tillman, F.A., & Lie, C.H. (1985). Fault tree analysis, methods, and applications: a review. IEEE Transactions on Reliability 34(3), 194–203.CrossRefGoogle Scholar
Leveson, N.G. (1995). Safeware: System Safety and Computers. Boston: Addison–Wesley.Google Scholar
Li, B., Li, M., Chen, K., & Smidts, C. (2006). Integrating software into PRA: a software-related failure mode taxonomy. Risk Analysis 26(4), 997–1012.CrossRefGoogle Scholar
Mosleh, A., Groen, F., Hu, Y., Nejad, H., Zhu, D., & Piers, T. (2004). Simulation-Based Probabilistic Risk Analysis Report. Center for Risk and Reliability, University of Maryland.Google Scholar
Mosterman, P.J., & Biswas, G. (1999). Diagnosis of continuous valued systems in transient operating regions. IEEE Transactions on Systems Man and Cybernetics: Part A Systems and Humans 29(6), 554–565.CrossRefGoogle Scholar
Mutha, C., Rodriguez, M., & Smidts, C.S. (2010 a). Software fault-failure and error propagation analysis using the unified modeling language. Proc. Int. Probabilistic Safety Assessment & Management Conf., Seattle, WA.Google Scholar
Mutha, C., Rodriguez, M., & Smidts, C.S. (2010 b). Design and analysis of safety critical software using UML. Proc. Man–Technology–Organization Sessions [HPR-372(2)].Google Scholar
Mutha, C., & Smidts, C.S. (2011). An early design stage UML-based safety analysis approach for high assurance software systems. IEEE Int. Symp. High-Assurance Systems Engineering, pp. 202–211, Boca Raton, FL.Google Scholar
NASA. (2004). NASA Software Safety Guidebook (NASA-GB-8719.13). Washington, DC: Author.Google Scholar
Nuclear Regulatory Commission. (1983). PRA Procedures Guide: A Guide to the Performance of Probabilistic Risk Assessments for Nuclear Power Plants (NUREG/CR-2300). Washington, DC: Nuclear Regulatory Commission.Google Scholar
Object Management Group. (2008). UML Profile Systems Modeling Language (SysML) Specification. Needham, MA: Object Management Group.Google Scholar
Object Management Group. (2009). UML 2 Superstructure Specification, v2.2. Needham, MA: Object Management Group .Google Scholar
Pahl, G., & Beitz, W. (1996). Engineering Design: A Systematic Approach. (Wallace, K., Ed.). New York: Springer.CrossRefGoogle Scholar
Rumbaugh, J., Jacobson, I., & Booch, G. (1999). The Unified Modeling Language Reference Manual, p. 30. Boston: Addison–Wesley.Google Scholar
Selonen, P., Koskimies, K., & Sakkinen, M. (2001). How to make apples from oranges in UML. Proc. Int. Conf. System Sciences 3, pp. 3054–3064.Google Scholar
Stone, R.B., Tumer, I.Y., & Van Wie, M. (2005). The function-failure design method. Journal of Mechanical Design 127(3), 397–407.CrossRefGoogle Scholar
Swaminathan, S., & Smidts, C.S. (1999). The event sequence diagram framework for dynamic probabilistic risk assessment, reliability engineering & system safety. Reliability Engineering and System Safety 63(1), 73–90.CrossRefGoogle Scholar
Towhidnejad, M., Wallace, D.R., Gallo, A.M., Goddard, N., & Flight, S. (2003). Fault tree analysis for software design. Proc. IEEE Software Engineering Workshop, pp. 24–29.Google Scholar
Tumer, I., & Smidts, C. (2011). Integrated design-stage failure analysis of software-driven hardware systems. IEEE Transactions on Computers 60(8), 1072–1084.CrossRefGoogle Scholar
Umeda, Y., & Tomiyama, T. (1997). Functional reasoning in design. IEEE Expert 12(2), 42–48.CrossRefGoogle Scholar
Whittle, J., & Schumann, J. (2000). Generating statechart designs from scenarios. Proc. Int. Conf. Software Engineering, ICSE'00 (Ghezzi, C., Jazayeri, M., & Wolf, A.L., Eds.), pp. 314–323.Google Scholar
Yairi, T., Kato, Y., & Hori, K. (2001). Fault detection by mining association rules from house-keeping data. Proc. Int. Symp. Artificial Intelligence Robotics and Automation in Space, Quebec.Google Scholar