Effects of faulty estimate in component reliability on system designing: a simulation approach

Document Type : Research Paper


School of Industrial Engineering, Islamic Azad University, South Tehran Branch, Tehran, Iran


Component reliability is usually estimated based on economical sampling plan and historical data analysis. In such process, two types of errors may occurs. According to a conventional view, the type 1 and 2 errors respectively referred to lower and higher component reliability estimation are arisen.  Generally, it is commonly thought that the first type error leads to an under-estimation of the whole system's reliability, and the second type over-estimates it, which in turn, causes false amplification or ignores the need to boost the system by using redundant components. This article is devoted to the role of component reliability estimation error in the design of a multi-state system (MSS). To this aim and from the literature survey, two optimal designed MSS evaluated by a proposed validated computer simulation model under assumption of positive and negative errors. Result revealed that any type of uncertain estimation increases with the over-designing risk and applying more number of components in the optimum system designing, but fortunately no weakness in its functionality. The greater the error, the more redundant components in MSS design.


Main Subjects

Ayensa Jimenez Jacobo, Doweidar Mohamed H, Sanz-Herrera Jose A and Doblare Manuel. (2018). A new reliability-based data-driven approach for noisy experimental data with physical constraints. Computer Methods Application in Mechanical Engineering. pp. 752-774.
Baesler F, Gatica J and Correa R. (2015). Simulation Optimization for Operating Room Scheduling. International Journal of Simulation Modelling. pp. 215-226.
Ibanez R, Abisset-Chavanne E, Aguado JV, Gonzalez DE and Chinesta Cueto F. (2016). A manifold learning approach to data-driven computational elasticity and inelasticity. Computational Methods in Engineering (Vol.1). pp. 1-11.
Jayalath DDACJ, Wimalaratne SPW and Karunananda AS. (2016). Modelling Goal Selection of Characters in Primary Groups in Crowd Simulations. International Journal of Simulation Modelling, 15(4), 585-596.
Kirchdoerfer T and Ortiz M. (2016). Data-driven computational mechanics. Computational Methods in Engineering. pp. 81-101.
Lisnianski A, Frenkel, I and Ding Y.( 2010). Multi-state System Reliability Analysis and Optimization for Engineers and Industrial Managers. London: Springer. New York: Dordrecht Heidelberg. British Library Cataloguing in Publication Data.
Li W, Zuo MJ.(2008). Reliability evaluation of multi-state weighted k-out-of-n systems. Reliability Engineering and System Safety. pp. 160–167.
Lalic B, Cosic I and Anisic Z.(2005). Simulation Based Design and Reconfiguration of Production Systems. pp. 173-183.
Ljoljic B, Katalinic B and Stuja K. (2002). Optimization of Flexible Assembly System using Simulation. International Journal of Simulation Modellin. 1(1), 16-22.
Lisnianski A, Levitin G, Ben-Haim H and Elmakis D. (1996). Power system structure optimization subject to reliability constraints. Electric Power Systems Research, 39, 145-152.
Pourhassan Mohammad Reza and Raissi Sadigh. (2017). An integrated simulation-based optimization technique for multi-objective dynamic facility layout problem. Journal of Industrial Information Integration, 8, 49-58.
Pan R, Zhang W, Yang S and Xiao Y. (2014). State Entropy Model integrated with BSC and ANP for Supplier Evaluation and Selection 2016: International Journal of Simulation Modelling, 13(3), 348-363.
Ramirez-Marquez JE and Coit, DW.  (2004). A monte-carlo simulation approach for approximating multi-state two-terminal reliability. Reliability Engineering & System Safety, 87, 253–64.
Ramirez Marquez JE and Coit, DW. (2004). A heuristic for solving the redundancy allocation problem for multi-state series-parallel systems. Reliability Engineering & System Safety, 83, 341-349.
Singh PK, Jain SC, and Jain PK. (2003). Tolerance Allocation with Alternative Manufacturing Process Suitability of Genetic Algorithm. International Journal of Simulation Modelling. pp. 22-34.
Simon E, Oyekan  J, Hutabarat W, Tiwari A and Turner CJ. (2018). Adapting Petri Nets to DES: Stochastic Modelling of Manufacturing Systems, (1), 5-17.
Tot N., Jovic F. and Slavek N. (2003). Simulation Model and Analysis of the Steam Turbine Subsystem.  International Journal of Simulation Modelling. pp. 43-49.
Yuan L.W., Li SM, Peng B. and Chen YM. (2015) Study on failure process of tailing Dams based on particle flow theories. International Journal of Simulation Modelling. pp. 658-668.
  • Receive Date: 06 November 2018
  • Revise Date: 13 December 2018
  • Accept Date: 08 May 2019
  • First Publish Date: 08 May 2019