Skip Navigation Links
Discontinuity, Nonlinearity, and Complexity

Dimitry Volchenkov (editor), Dumitru Baleanu (editor)

Dimitry Volchenkov(editor)

Mathematics & Statistics, Texas Tech University, 1108 Memorial Circle, Lubbock, TX 79409, USA

Email: dr.volchenkov@gmail.com

Dumitru Baleanu (editor)

Cankaya University, Ankara, Turkey; Institute of Space Sciences, Magurele-Bucharest, Romania

Email: dumitru.baleanu@gmail.com


Performance Analysis of Embedded System with Failure Interaction and Repair Discipline Using Copula

Discontinuity, Nonlinearity, and Complexity 13(1) (2024) 1--15 | DOI:10.5890/DNC.2024.03.001

Elsayed E. Elshoubary$^{1}$, Mohammed Alqawba$^{2}$, Taha Radwan$^{2,3}$

$^{1}$ Department of Basic Science, El-Ahram Institute for Engineering and Technology, Cairo, Egypt

$^{2}$ Department of Mathematics, College of Science and Arts, Qassim University, Ar Rass, Saudi Arabia

$^{3}$ Department of Mathematics and Statistics, Faculty of Management Technology and Information Systems, Port Said University, Port Said, Egypt

Download Full Text PDF

 

Abstract

This paper applies reliability analysis to the fuel control system of an airplane. The rates of transition to fail states follow exponential distributions. Two types of repair are compared, with the Gumbel-Hougaard copula yielding higher profits under our model. General repair is sufficient while a system is working in a degraded state. However, if the system is fully failing, copula distribution should be used for quick system maintenance. Reliability indicators are computed over time, including availability, mean time to failure, and sensitivity. To develop explicit formulations for profit, mean time to failure, and steady-state availability, differential difference equations are constructed and resolved. The system is solved using a supplementary variable technique. Numerical results were generated using the Mathematica software.

References

  1. [1] Iqbal, M.Z., Arcuri, A., and Briand, L. (2015), Environment modeling and simulation for automated testing of soft real-time embedded software, Software and Systems Modeling, 14, 483–524.
  2. [2] Iyer, R.K. and Velardi, P. (1985), Hardware-related software errors: measurement and analysis, IEEE Transactions on Software Engineering, (2), 223–231.
  3. [3]  Mittal, S. (2019), A survey on modeling and improving reliability of DNN algorithms and accelerators, Journal of Systems Architecture, 101689.
  4. [4] Avizienis, A., Laprie, J.C., Randell, B., and Landwehr, C. (2004), Basic concepts and taxonomy of dependable and secure computing, IEEE Transactions on Dependable and Secure Computing, 1, 11–33.
  5. [5]  Teng, X., Pham, H., and Jeske, D/R. (2006), Reliability modeling of hardware and software interactions, and its applications, IEEE Transactions on Reliability, 55, 571–577.
  6. [6] Costes, A., Landrault, C., and Laprie, J.C. (1978), Reliability and availability models for maintained systems featuring hardware failures and design faults, IEEE Transactions on Computers, 100(6), 548-560.
  7. [7]  Sumita, U. and Masuda, Y. (1986), Analysis of software availability/reliability under the influence of hardware failures, IEEE Transactions on Software Engineering, (1), 32-41.
  8. [8]  Diao, X., Zhao, Y., Pietrykowski, M., Wang, Z., Bragg-Sitton, S., and Smidts, C. (2018), Fault propagation and effects analysis for designing an online monitoring system for the secondary loop of the nuclear power plant portion of a hybrid energy system, Nuclear Technology, 202, 106–123.
  9. [9] Yusuf, I., Ismail, A.L., Singh, V.V., Ali, U.A., and Sufi, N.A. (2020), Performance analysis of multi-computer system consisting of three subsystems in series configuration using copula repair policy, SN Computer Science, 1(5), 1–11.
  10. [10] Park, J. and Baik, J. (2015), Improving software reliability prediction through multi- criteria based dynamic model selection and combination, Journal of Systems and Software, 101, 236–244. https://doi.org/10.1016/j.jss.2014.12.029.
  11. [11] Lung, C. H., Zhang, X., and Rajeswaran, P. (2016), Improving software performance and reliability in a distributed and concurrent environment with an architecture-based selfadaptive framework, Journal of Systems and Software, 121, 311–328. https://doi.org/10.1016/j.jss.2016.06.102.
  12. [12] Gahlot, M., Singh, V.V., Ayagi, H.I., and Abdullahi, I. (2020), Stochastic analysis of a two units' complex repairable system with Switch and Human failure using Copula approach, Life Cycle Reliability and Safety Engineering, 9(1), 1–11. https://doi.org/10.1007/s41872-019-00103-1.
  13. [13] Singh, V.V., Gulati, J., Rawal, D.K., and Goel, C.K. (2016), Performance analysis of complex system in series configuration under different failure and repair discipline using copula, International Journal of Reliability, Quality and Safety Engineering, 23(2), 812–832.
  14. [14] Singh, V.V., Poonia, P.K., and Abdullahi, A.F. (2020), Performance analysis of a complex repairable system with two subsystems in series configuration with an imperfect switch, Journal of Mathematics and Computer Science, 10(2), 359–383.
  15. [15] Sinha, S., Goyal, N.K., and Mall, R. (2021), Reliability and availability prediction of embedded systems based on environment modeling and simulation, Simulation Modelling Practice and Theory, 108, 102246.