A Model-Based Approach for Reliability Assessment in Component- Based Systems



Published Sep 29, 2014
Saideep Nannapaneni Abhishek Dubey Sherif Abdelwahed Sankaran Mahadevan Sandeep Neema


This paper describes a formal framework for reliability assessment of component-based systems with respect to specific missions. A mission comprises of different timed mission stages, with each stage requiring a number of high- level functions. The work presented here describes a modeling language to capture the functional decomposition and missions of a system. The components and their alternatives are mapped to basic functions which are used to implement the system-level functions. Our contribution is the extraction of mission-specific reliability block diagram from these high-level models of component assemblies. This is then used to compute the mission reliability using reliability information of components. This framework can be used for real-time monitoring of system performance where reliability of the mission is computed over time as the mission is in progress. Other quantities of interest such as mission feasibility, function availability can also be computed using this framework. Mission feasibility answers the question whether the mission can be accomplished given the current state of components in the system and function availability provides information if the function is available in the future given the current state of the system. The software used in this framework includes Generic Modeling Environment (GME) and Python. GME is used for modeling the system and Python for reliability computations. The proposed methodology is demonstrated using a radio-controlled (RC) car in carrying out a simple surveillance mission.

How to Cite

Nannapaneni, S., Dubey, A. ., Abdelwahed, S. ., Mahadevan, S., & Neema, S. . (2014). A Model-Based Approach for Reliability Assessment in Component- Based Systems. Annual Conference of the PHM Society, 6(1). https://doi.org/10.36001/phmconf.2014.v6i1.2394
Abstract 357 | PDF Downloads 192



Real-Time Monitoring, Component-based systems, Reliability Assessment

Bennetts, R. G. (1982). Analysis of reliability block diagrams by Boolean techniques. IEEE Transactions on Reliability, 31(2), 159-166.

Bouti, A., & Kadi, D. A. (1994). A state-of-the-art review of FMEA/FMECA. International Journal of reliability, quality and safety engineering, 1(04), 515-543.

Dubey, A., Mahadevan, N., & Karsai, G. (2012). The inertial measurement unit example: A software health management case study. ISIS, 12, 101.

Elsayed, E. A. (2012). Reliability engineering. Wiley Publishing.

Ericson, C. A. (2005). Event Tree Analysis. Hazard Analysis Techniques for System Safety, 223-234.

Filliben, J. J. (2002). NIST/SEMTECH Engineering Statistics Handbook. Gaithersburg: www. itl. nist. gov/div898/handbook, NIST.

Greenfield, M. A. (2001). NASA's use of quantitative risk assessment for safety upgrades. Space safety, rescue and quality, 153-159.

Kececioglu, D. (1972). Reliability analysis of mechanical components and systems. Nuclear Engineering and Design, 19(2), 259-290.

Krishnamurthy, S., & Mathur, A. P. (1997). On the estimation of reliability of a software system using reliabilities of its components. Proceedings of 8th International Symposium in Software Reliability Engineering (pp. 146-155). IEEE.

Kurtoglu, T., & Tumer, I. Y. (2008). A graph-based fault identification and propagation framework for functional design of complex systems. Journal of Mechanical Design, 130, 051401.

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.

Ledeczi, A., Maroti, M., Bakay, A., Karsai, G., Garrett, J., Thomason, C. & Volgyesi, P. (2001). The generic modeling environment. Workshop on Intelligent Signal Processing, Budapest, Hungary (Vol. 17).

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.

Mahadevan, N., Dubey, A., Balasubramanian, D., & Karsai, G. (2013). Deliberative, search-based mitigation strategies for model-based software health management. Innovations in
Systems and Software Engineering, 9(4), 293-318.

Modarres, M. (2008). Probabilistic Risk Assessment. Handbook of Performability Engineering (pp. 699-718). Springer London.

Mosterman, P. (2007). Model-based design of embedded systems. IEEE International Conference on Microelectronic Systems Education, IEEE.

Phillips, A. M. (2002). Functional decomposition in a vehicle control system. Proceedings of American Control Conference (Vol. 5, pp. 3713-3718). IEEE.

Python library for Electronic Design Automation (PyEDA) Documentation [Online]. https://media.readthedocs.org/pdf/pyeda/latest/pyeda.pd f. Last accessed – May 30, 2014

Schattkowsky, T., & Muller, W. (2004). Model-based design of embedded systems. Proceedings of Seventh IEEE International Symposium on Object-Oriented Real-Time Distributed Computing (pp. 113-128). IEEE.

Teng, S. H. G., & Ho, S. Y. M. (1996). Failure mode and effects analysis: an integrated approach for product design and process control. International Journal of Quality & Reliability Management, 13(5), 8-26.

Wood, A. P. (2001). Reliability-metric varieties and their relationships. Proceedings of Reliability and Maintainability Symposium (pp. 110-115). IEEE.
Technical Research Papers

Most read articles by the same author(s)

1 2 > >>