Hananeh Aliee
Michael Glaß
ABSTRACT
Importance measure analysis judges the relative importance of components in a system and reveals how each component contributes to the system reliability. In the design of large and complex systems, importance measure analysis can therefore be employed to guide an optimization tool which design decisions to investigate to gain higher reliability. While previous research has mainly concentrated on developing analytical importance measure techniques, the automatic and frequent computing of importance measures as required in the context of design space exploration has got very few, if any attention. This paper presents a highly efficient technique to compute the reliability and structural importance measures of components of a system. The proposed technique considers the reliability of a system implementation and subsequently analyzes the importance measures of its components based on a state-of-the-art Monte Carlo simulation. The technique can therefore estimate the importance measures of all components concurrently, highly improving the performance of the computation compared, e. g., to the well-known Birnbaum approach by the factor of 2n with n being the number of components. Moreover, we show how this algorithm can be extended to support importance measure analysis in the existence of transient faults which is essential since in future systems, transient faults are expected to cause relatively more failures than permanent faults. We integrated the proposed analysis approach in an existing multi-objective local-search algorithm that is part of an automatic system-level design space exploration which seeks for system implementations with highest reliability at lowest possible cost. Experimental results show that the proposed algorithm performs efficiently with negligible imprecision, even for large real-world examples.
- N. Miskov-Zivanov and D. Marculescu, "Multiple transient faults in combinational and sequential circuits: A systematic approach," IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 29, no. 10, pp. 1614--1627, 2010. Google Scholar
Digital Library
- M. Glaß, M. Lukasiewycz, T. Streichert, C. Haubelt, and J. Teich, "Reliability-aware system synthesis," in Design, Automation Test in Europe, 2007, pp. 1--6.Google Scholar
- S. Beeson and J. Andrews, "Importance measures for non-coherent-system analysis," IEEE Transactions on Reliability, vol. 52, no. 3, pp. 301--310, 2003.Google ScholarCross Ref
- Z. Birnbaum, On the Importance of Different Components in a Multicomponent System. Academic Press, 1969.Google Scholar
- W. Kuo and X. Zhu, "Relations and generalizations of importance measures in reliability," IEEE Transactions on Reliability, vol. 61, no. 3, pp. 659--674, 2012.Google ScholarCross Ref
- M. Marseguerra and E. Zio, "Monte carlo estimation of the differential importance measure: Application to the protection system of a nuclear reactor," Reliability Engineering and System Safety, vol. 86, no. 1, pp. 11--24, 2004.Google ScholarCross Ref
- D. Wang and K. Trivedi, "Computing steady-state mean time to failure for non-coherent repairable systems," IEEE Transactions on Reliability, vol. 54, no. 3, pp. 506--516, 2005.Google ScholarCross Ref
- H. Aliee and H. Zarandi, "Fault tree analysis using stochastic logic: A reliable and high speed computing," in Annual Reliability and Maintainability Symposium (RAMS). USA: IEEE Computer Society, 2011, pp. 1--6.Google Scholar
- F. Khosravi, F. Reimann, M. Glass, and J. Teich, "Multi-objective local-search optimization using reliability importance measuring," in Proceedings of Design Automation Conference (DAC), 2014. Google ScholarDigital Library
- W. Kuo and X. Zhu, Importance Measures in Reliability, Risk, and Optimization: Principles and Applications. United Kingdom: John Wiley and Sons, 2012. Google ScholarDigital Library
- J. Fussell, "How to hand-calculate system reliability and safety characteristics," IEEE Transactions on Reliability, vol. R-24, no. 3, pp. 169--174, 1975.Google ScholarCross Ref
- J. Fussell and W. Vesely, "A new methodology for obtaining cut sets for fault trees," Transactions of the American Nuclear Society, vol. 15, no. 1, pp. 262--263, 1972.Google Scholar
- J. Walter and J. Talmor, "Reliability Analysis of Avionics in the Commercial Aerospace Industry," The Journal of the Reliability Analysis Center, vol. 1, 2005.Google Scholar
- F. Meng, "Relationships of fussell-vesely and birnbaum importance to structural importance in coherent systems," Reliability Engineering and System Safety, vol. 67, no. 1, pp. 55--60, 2000.Google ScholarCross Ref
- D. Deeter and A. Smith, "Heuristic optimization of network design considering all-terminal reliability," in Reliability and Maintainability Symposium (RAMS), 1997, pp. 194--199.Google Scholar
- D. Coit, T. Jin, and N. Wattanapongsakorn, "System optimization with component reliability estimation uncertainty: A multi-criteria approach," IEEE Transactions on Reliability, vol. 53, no. 3, pp. 369--380, 2004.Google ScholarCross Ref
- H. Tekiner-Mogulkoc and D. Coit, "System reliability optimization considering uncertainty: Minimization of the coefficient of variation for series-parallel systems," IEEE Transactions on Reliability, vol. 60, no. 3, pp. 667--674, 2011.Google ScholarCross Ref
- R. Barlow and F. Proschan, Statistical Theory of Reliability and Life Testing: Probability Models. Silver Spring, MD 20904: To Begin With, 1981.Google Scholar
- F. Meng, "Comparing the importance of system components by some structural characteristics," IEEE Transactions on Reliability, vol. 45, no. 1, pp. 59--65, 1996.Google ScholarCross Ref
- E. Borgonovo, "The reliability importance of components and prime implicants in coherent and non-coherent systems including total-order iterations," European Journal of Operational Research, vol. 204, no. 3, pp. 485--495, 2010.Google ScholarCross Ref
- H. Aliee, M. Glaß, F. Reimann, and J. Teich, "Automatic Success Tree-Based Reliability Analysis for the Consideration of Transient and Permanent Faults," in Proceedings of Design, Automation, and Test in Europe (DATE 2013), 2013, pp. 1621--1626. Google ScholarDigital Library
- W. Vesely, M. Stamatelatos, J. Dugan, J. Fragola, J. Minarick, and J. Railsback, Fault Tree Handbook with Aerospace Applications. NASA Office of Safety and Mission Assurance, 2002, pp. 1--218.Google Scholar
- T. Blickle, J. Teich, and L. Thiele, "System-level synthesis using evolutionary algorithms," Design Automation for Embedded Systems, vol. 3, no. 1, pp. 23--58, 1998.Google ScholarDigital Library
- J. Teich, "Hardware/software co-design: Past, present, and predicting the future," Proceedings of the IEEE, vol. 100, no. 5, pp. 1411--1430, 2012.Google ScholarCross Ref
- K. Deb, S. Agrawal, A. Pratap, and T. Meyarivan, "A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II," in the 6th International Conference on Parallel Problem Solving from Nature, 2000, pp. 849--858. Google ScholarDigital Library
- M. Laumanns, L. Thiele, K. Deb, and E. Zitzler, "Combining convergence and diversity in evolutionary multiobjective optimization," Evolutionary computation, vol. 10, pp. 263--282, 2002. Google ScholarDigital Library
Index Terms
- An efficient technique for computing importance measures in automatic design of dependable embedded systems
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