Introducing probabilistic models for redundant system reliability

• Autorzy: Babagana M. , Gimba B. , Yusuf I.

Identyfikatory

DOI

10.5277/ord160101

• Języki publikacji: EN

Abstrakty

EN

Probabilistic models have been developed to evaluate the relationship between reliability measures and the performance of a repairable network with built in redundancy. Networks with built in redun-dancy have been considered and explicit expressions have been derived for three characteristics related to such systems including steady-state availability, period of repair, and a profit function. Various graphs have been plotted to discover the impact of availability and mean time to system failure on net profit, as well as the impact of the failure and service rate on the steady-state availability, net profit and mean time to system failure. The system was analysed using first order linear differential equations.

Słowa kluczowe

EN

reliability; availability; mean time to system failure (MTSF); busy period; network; redundancy

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Operations Research and Decisions
• Rocznik: 2016
• Tom: Vol. 26, No. 1
• Strony: 5--18
• Opis fizyczny: Bibliogr. 11 poz., rys.

Twórcy

autor

Babagana M.

• Department of Computer Science, Bayero University, Kano, Nigeria

autor

Gimba B.

• Department of Mathematics, Federal University, Dutse, Nigeria

autor

Yusuf I.

• Department of Mathematical Sciences, Bayero University, Kano, Nigeria

Bibliografia

• [1] ALI P., Reliability of wireless body area networks used for ambulatory monitoring and health care, Life Science Journal, 2009, 6 (4), 5.
• [2] EL-SAID K.M., EL-SHERBENY M.S., Evaluation of reliability and availability characteristics of two dif-ferent systems by using linear first order differential equations, Journal of Mathematics and Statistics, 2005, 1 (2), 119.
• [3] FATHABADI H.S., KHODAEI M., Reliability evaluation of network flows with stochastic capacity and cost constraint, International Journal of Mathematics in Operational Research, 2012, 4 (4), 439.
• [4] HAGGAG M.Y., Cost analysis of a system involving common cause failures and preventive mainte-nance, Journal of Mathematics and Statistics, 2009, 5 (4), 305.
• [5] HASSAN M., Reliability evaluation of stochastic-flow network under quickest path and system capacity constraints, International Journal of Computer Networks, 2012 (4), 98.
• [6] LIN Y.-K., System reliability of a limited-flow network in multi commodity case. Reliability, IEEE Transactions, 2007, 56 (1), 17.
• [7] ROCCO S.C.M., ZIO E., Solving advanced network reliability problems by means of cellular automata and Monte Carlo sampling, Reliability Engineering and System Safety, 2005, 89 (2), 219.
• [8] ROCCO S.C.M., MORENO J.A., Network reliability assessment using a cellular automata approach, Reliability Engineering and System Safety, 2002, 78 (3), 289.
• [9] VASAR C., PROSTEAN O., FILIP I., ROBU R., POPESCU D., Markov models for wireless sensor network reliability, Proc. of IEEE ICCP, 2009, 323.
• [10] WANG K.-H., KUO C.-C., Cost and probabilistic analysis of series systems with mixed standby compo-nents, Applied Mathematical Modelling, 2000, 24, 957.
• [11] WANG K.-H., HSIEH C.-H., LIOU C.-H., Cost benefit analysis of series systems with cold standby com-ponents and a repairable service station, 2006, 3 (1), 77.

Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.