Abstract
Phased-mission systems (PMSs) have wide applications in engineering practices, such as manmade satellites. Certain critical parts in the system, such as cold standby, hot standby and functional standby, are designed in redundancy architecture to achieve high reliability performance. State-space models such as Markov process have been used extensively in previous studies for reliability evaluation of PMSs with dynamic behaviors. The most popular way to deal with the dynamic behaviors is Markov process, but it is well known that Markov process is limited to exponential distribution. In practice, however, the lifetime of most machinery products can follow non-exponential distributions like the Weibull distribution which cannot be handled by the Markov process. In order to solve this kind of problem, we present a semi-Markov model combined with an approximation algorithm to analyze PMS reliability subjected to non-exponential failures. Furthermore, the accuracy of the approximation algorithm is investigated by comparing to an accurate solution, and a typical PMS (attitude and orbit control system) is analyzed to demonstrate the implementation of the method.
Similar content being viewed by others
References
XING L D, BECHTA DUGAN J B. Analysis of generalized phased-mission system reliability, performance, and sensitivity [J]. IEEE Transactions on Reliability, 2002, 51(2): 199–211.
ZANG X Y, SUN H R, TRIVEDI K S. A BDD-based algorithm for reliability analysis of phased-mission systems [J]. IEEE Transactions on Reliability, 1999, 48(1): 50–60.
OU Y, DUGAN J B. Modular solution of dynamic multi-phase systems [J]. IEEE Transactions on Reliability, 2004, 53(4): 499–508.
LEVITIN G, XING L D, AMARI S V. Recursive algorithm for reliability evaluation of non-repairable phased mission systems with binary elements [J]. IEEE Transactions on Reliability, 2012, 61(2): 533–542.
XING L D, MESHKAT L, DONOHUE S K. Reliability analysis of hierarchical computer-based systems subject to common-cause failures [J]. Reliability Engineering and System Safety, 2007, 92: 351–359.
BOBBIO A, PREMOLI A, SARACCO O. Multi-state homogeneous Markov models in reliability analysis [J]. Microelectronics Reliability, 1980, 20(6): 875–880.
ERYILMAZ S. Modeling dependence between two multi-state components via copulas [J]. IEEE Transactions on Reliability, 2014, 63(3): 715–720.
DHOPLE SV, DOMíNGUEZ-GARCíA A D. A parametric uncertainty analysis method for Markov reliability and reward models [J]. IEEE Transactions on Reliability, 2012, 61(3): 634–648.
XING L D, AMARI S V. Reliability of phased-mission systems [C]//Handbook of Performability Engineering. London: Springer, 2008: 349–368.
DISTEFANO S, TRIVEDI K S. Non-Markovian statespace models in dependability evaluation [J]. Quality and Reliability Engineer International, 2013, 29(2): 225–239.
DUTUIT Y, RAUZY A. A linear-time algorithm to find modules of fault trees [J]. IEEE Transactions on Reliability, 1996, 45(3): 422–425.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Zhou, H., Li, X. & Huang, H. Approximate method for reliability assessment of complex phased mission systems. J. Shanghai Jiaotong Univ. (Sci.) 22, 247–251 (2017). https://doi.org/10.1007/s12204-017-1828-2
Received:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12204-017-1828-2