Risk assessment of systems during development phase: an approach based on lifetime functions


  • Mahomed Soliman
  • Dan M. Frangopol


risk assessment, lifetime function, optimization


Quantifying the time-dependent risk associated with complex engineering projects is an essential task to ensure the success of these projects. Low technology readiness and maturity levels and uncertainties associated with different development scenarios and management decisions are among the risk mechanisms which may affect a system. Accordingly, monitoring the risk associated with a project during its development phase is necessary in order to achieve satisfactory outcomes. This paper investigates the applicability of system reliability concepts in assessing the risk associated with engineering systems during their development phase. A system-based approach to quantify risk associated with a systems, based on time-dependent system reliability principles applied through lifetime functions, is proposed. The approach starts with defining the probabilistic parameters associated with the development time of each component in the system. A measure of time-dependent system performance is established next and combined with the project consequences to compute the time-dependent system risk. In the presence of multiple scenarios associated with system development, risk profiles of each scenario are identified and compared. Moreover, a bi-objective optimization problem is formulated and solved to obtain the development scenarios which provide the lowest risk while minimizing the system development cost.


CONROW, E.H., Effective Risk Management: Some Keys to Success, 2nd Edition, American Institute of Aeronautics and Astronautics, Reston, VA, 2003.

KUJAWSKI, Edouard, ANGELIS, Diana, Monitoring Risk Response Actions for Effective Project Risk Management, Sys. Eng., 13, 4, pp. 353?368, 2010.

USDOE, Technology readiness assessment guide, Report Number DOE G 413.3-4A, U.S. Department of Energy, Washington, D.C., 2011.

SAUSER, B., System Maturity and Architecture: Assessment Methods, Processes, and Tools, Final Technical Report SERC-2012-TR-027, Systems Engineering Research Center, Stevens Institute of Technology, Hoboken, NJ, 2012.

HICKS, B., LARSSON, A., CULLEY, S., LARSSON, T., A Methodology for Evaluating Technology Readiness During Project Development, The 17th International Conference on Engineering Design, Stanford University, Stanford, CA, August 24–27, 2009.

MARVEL, J., EASTMAN, R., CHEOK, G., SAIDI, K., HONG, T., MESSINA, E., Technology Readiness Levels for Randomized Bin Picking, NISTIR 7876, National Institute of Standards and Technology, U.S. Department of Commerce, Washington, D.C., 2012.

DUBOS, G.F., SALEH, J.H., BRAUN, R., Technology Readiness Level, Schedule Risk and Slippage in Spacecraft Design: Data Analysis and Modeling, AIAA SPACE 2007 Conference & Exposition, Long Beach, CA, September 18-20, 2007.

SMITH, J.D., An Alternative to Technology Readiness Levels for Non-developmental Item (NDI) Software, Technical Report CMU/SEI-2004-TR-013, Carnegie Mellon Software Engineering Institute, Pittsburgh, PA, 2004.

SMITH, J.D., ImpACT: An Alternative to Technology Readiness Levels for Commercial Off-theshelf (COTS) Software, Carnegie Mellon Software Engineering Institute, Pittsburgh, PA, 2004.

MCNAMARA, K.M., NASA Technology Readiness Levels: Relevance to Manufacturing, The 2012 Performance Metrics for Intelligent Systems Workshop, Appendix B – NISTIR 7876, National Institute of Standards and Technology, U.S. Department of Commerce, Washington, D.C, 2012.

ATWATER, B., Personal Communication with the authors, Lehigh University, September 10, 2013.

LEEMIS, L.M. Reliability, Probabilistic Models and Statistical Methods, Prentice Hall, New

Jersey, 1995.

OKASHA, Nader M., FRANGOPOL, Dan M., Novel Approach for Multi-criteria Optimization of Life-cycle Preventive and Essential Maintenance of Deteriorating Structures, J. Struct. Eng., 136, 8, pp. 1009?1022, 2010.

RAUSAND M., HØYLAND, A., System Reliability Theory: Models, Statistical Methods, and Applications, John Wiley & Sons, New York, 2004.

MATHWORKS Inc., Global Optimization ToolboxTM User’s Guide, Version 3.2.4, MathWorks Inc, Natick, MA, 2013.

DEB, K., Multi-objective Optimization using Evolutionary Algorithms. John Wiley & Sons, New York, 2001.

ARORA, J., Introduction to Optimum Design, 3rd Edition, Elsevier Academic Press, New York, 2011.