Abstract
In recent years, research implications of reliability, availability, and maintainability (RAM) aspects of reliability engineering systems have increased substantially due to rising operating and maintenance costs. For industrial systems, the cost is considered to be the most significant factor and RAM is an increasingly important issue for determining the performance of the system. On the other hand, the information available from the collected databases or records is most of the time imprecise, limited, and uncertain, and the management decisions are based on experience. Thus it is difficult for job analysts to analyze the performance of the system by utilizing these uncertain data. Therefore, the objective of this chapter is to quantify the uncertainties that make the decisions realistic, generic, and extensible for the application domain. For this, an optimization model has been constructed by taking composite measure of RAM parameters called RAM index and system cost as an objective function and solved with evolutionary techniques algorithm. The obtained failure rates and repair times of all constituent components are used for measuring the performance of the system in terms of various reliability parameters using intuitionistic fuzzy set theory and weakest t-norm based arithmetic operations. Performance analysis on system RAM index has also been analyzed to show the effect of taking wrong combinations of their reliability parameters on its performance. The suggested framework has been illustrated with the help of a case.
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Abbreviations
- \(\tilde{A}\) :
-
Fuzzy set
- \(\mu_{{\tilde{A}}}\) :
-
Membership functions of fuzzy set \(\tilde{A}\)
- \(\upsilon_{{\tilde{A}}}\) :
-
Nonmembership functions of fuzzy set \(\tilde{A}\)
- \(\tilde{\lambda }_{i}\) :
-
Fuzzy failure rate of ith component
- \(\tilde{{T_{i} }}\) :
-
Fuzzy repair time of ith component
- \(A^{\left( \alpha \right)}\) :
-
Alpha-cut of the fuzzy set
- \({\text{MTBF}}_{i}\) :
-
Mean time between failures of the ith components
- \({\text{MTTR}}_{i}\) :
-
Mean time to repair of the ith components
- \({\text{CMTBF}}_{i}\) :
-
Cost of mean time between failures of the ith components
- \({\text{CMTTR}}_{i}\) :
-
Cost of mean time to repair of the ith components
- \({\text{LbMTBF}}_{i}\) :
-
Lower limit of the mean time between failures of the ith components
- \({\text{UbMTBF}}_{i}\) :
-
Upper limit of the mean time between failures of the ith components
- \({\text{LbMTTR}}_{i}\) :
-
Lower limit of mean time to repair of the ith components
- \({\text{UbMTTR}}_{i}\) :
-
Upper limit of mean time to repair of the ith components
- \(R_{\text{s}}\) :
-
System reliability
- \(A_{\text{s}}\) :
-
System availability
- \(M_{\text{s}}\) :
-
System maintainability
- \({\text{iter}}\) :
-
Current iteration number
- \({\text{iter}}_{\hbox{max} }\) :
-
Maximum iteration number
- \(T_{\omega }\) :
-
Weakest t-norm
- \({\text{TFN}}\) :
-
Triangular fuzzy number
- \({\text{IFN}}\) :
-
Intuitionistic fuzzy number
- \(\alpha_{i} ,\,\beta_{i} ,\,\gamma_{i}\) :
-
Physical feature of each component
- \(c_{1}\) :
-
Individual intelligence coefficient
- \(c_{2}\) :
-
Social intelligence coefficient
References
Attanassov, K. T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems, 20, 87–96.
Attanassov, K. T. (1989). More on intuitionistic fuzzy sets. Fuzzy Sets and Systems, 33(1), 37–46.
Brest, J., Greiner, S., Boskovic, B., Mernik, M., & Zumer, V. (2006). Self- adapting control parameters in differential evolution: A comparative study on numerical benchmark problems. Transactions on Evolutionary Computation, 10(6), 646–657.
Bris, R., Chatelet, E., & Yalaoui, F. (2003). New method to minimize the preventive maintenance cost of series-parallel systems. Reliability Engineering and System Safety, 82, 247–255.
Bustince, H., & Burillo, P. (1996). Vague sets are intuitionistic fuzzy sets. Fuzzy Sets and Systems, 79(3), 403–405.
Chang, J. R., Chang, K. H., Liao, S. H., & Cheng, C. H. (2006). The reliability of general vague fault tree analysis on weapon systems fault diagnosis. Soft Computing, 10, 531–542.
Chen, S. M. (2003). Analyzing fuzzy system reliability using vague set theory. International Journal of Applied Science and Engineering, 1(1), 82–88.
Coelho, L. S. (2009). An efficient particle swarm approach for mixed-integer programming in reliability redundancy optimization applications. Reliability Engineering and System Safety, 94(4), 830–837.
Eberhart, R., & Kennedy, J. (1995). A new optimizer using particle swarm theory. In Proceedings of the Sixth International Symposium on Micro Machine and Human Science (pp. 39–43).
Garg, H. (2013). Reliability analysis of repairable systems using Petri nets and Vague Lambda-Tau methodology. ISA Transactions, 52(1), 6–18.
Garg, H., & Rani, M. (2013). An approach for reliability analysis of industrial systems using PSO and IFS technique. ISA Transactions, 52(6), 701–710.
Garg, H., & Sharma, S. P. (2012). A two-phase approach for reliability and maintainability analysis of an industrial system. International Journal of Reliability, Quality and Safety Engineering, 19(3), 1250013 (19 pages).
Garg, H., & Sharma, S. P. (2012). Stochastic behavior analysis of industrial systems utilizing uncertain data. ISA Transactions, 51(6), 752–762.
Garg, H., & Sharma, S. P. (2013). Multi-objective reliability-redundancy allocation problem using particle swarm optimization. Computers & Industrial Engineering, 64(1), 247–255.
Garg, H., Sharma, S. P., & Rani, M. (2012). Cost minimization of washing unit in a paper mill using artificial bee colony technique. International Journal of System Assurance Engineering and Management, 3(4), 371–381.
Garg, H., Rani, M., & Sharma, S. P. (2013a). Predicting uncertain behavior of press unit in a paper industry using artificial bee colony and fuzzy Lambda-Tau methodology. Applied Soft Computing, 13(4), 1869–1881.
Garg, H., Rani, M., & Sharma, S. P. (2013). Reliability analysis of the engineering systems using intuitionistic fuzzy set theory. Journal of Quality and Reliability Engineering, Article ID 943972, 10 pages.
Garg, H., Rani, M., & Sharma, S. P. (2013). Preventive maintenance scheduling of the pulping unit in a paper plant, Japan. Journal of Industrial and Applied Mathematics, 30(2), 397–414.
Garg, H., Rani, M., & Sharma, S. P. (2013). Predicting uncertain behavior and performance analysis of the pulping system in a paper industry using PSO and Fuzzy methodology. In P. Vasant (Ed.), Handbook of Research on Novel Soft Computing Intelligent Algorithms: Theory and Practical Applications (pp. 414–449). IGI Global, USA.
Garg, H., Rani, M., Sharma, S. P., & Vishwakarma, Y. (2014a). Intuitionistic fuzzy optimization technique for solving multi-objective reliability optimization problems in interval environment. Expert Systems with Applications, 41, 3157–3167.
Garg, H., Rani, M., & Sharma, S. P. (2014b). An approach for analyzing the reliability of industrial systems using soft computing based technique. Expert Systems with Applications, 41, 489–501.
Gau, W. L., & Buehrer, D. J. (1993). Vague sets. IEEE Transaction on Systems, Man, and Cybernetics, 23, 610–613.
Gen, M., & Yun, Y. S. (2006). Soft computing approach for reliability optimization: State-of-the-art survey. Reliability Engineering and System Safety, 91(9), 1008–1026.
Goldberg, D. E. (1989). Genetic algorithm in search, optimization and machine learning. MA: Addison-Wesley.
Holland, J. H. (1975). Adaptation in natural and artificial systems, Ann Arbor. MI: The University of Michigan Press.
Hsieh, T.-J., & Yeh, W.-C. (2012). Penalty guided bees search for redundancy allocation problems with a mix of components in series parallel systems. Computers & Operations Research, 39(11), 2688–2704.
Juang, Y. S., Lin, S. S., & Kao, H. P. (2008). A knowledge management system for series-parallel availability optimization and design. Expert Systems with Applications, 34, 181–193.
Karaboga, D. (2005). An idea based on honey bee swarm for numerical optimization. Tech. rep., TR06, Erciyes University, Engineering Faculty, Computer Engineering Department.
Karaboga, D., & Akay, B. (2009). A comparative study of artificial bee colony algorithm. Applied Mathematics and Computation, 214(1), 108–132.
Karaboga, D., & Basturk, B. (2007). A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm. Journal of Global Optimization, 39, 459–471.
Karaboga, D., & Ozturk, C. (2011). A novel clustering approach: Artificial bee colony (ABC) algorithm. Applied Soft Computing, 11(1), 652–657.
Kennedy, J., & Eberhart, R. C. (1995) Particle swarm optimization. In IEEE International Conference on Neural Networks, Vol. IV (pp. 1942–1948). Piscataway.
Knezevic, J., & Odoom, E. R. (2001). Reliability modeling of repairable systems using Petri nets and Fuzzy Lambda-Tau Methodology. Reliability Engineering and System Safety, 73(1), 1–17.
Komal, Sharma, S. P., & Kumar, D. (2010). RAM analysis of repairable industrial systems utilizing uncertain data. Applied Soft Computing, 10, 1208–1221.
Kuo, W., Prasad, V. R., Tillman, F. A., & Hwang, C. (2001). Optimal reliability design: Fundamentals and applications. Cambridge: Cambridge University Press.
Lapa, C. M. F., Pereira, C. M., & Barros, M. P. D. (2006). A model for preventive maintenance planning by genetic algorithms based on cost and reliability. Reliability Engineering and System Safety, 91, 233–240.
Leou, R. (2006). A method for unit maintenance scheduling considering reliability and operation expense. Electrical Power and Energy Systems, 28, 471–481.
Rajpal, P. S., Shishodia, K. S., & Sekhon, G. S. (2006). An artificial neural network for modeling reliability, availability and maintainability of a repairable system. Reliability Engineering and System Safety, 91(7), 809–819.
Ross, T. J. (2004). Fuzzy logic with engineering applications, 2nd edn. New York: Wiley.
Saraswat, S., & Yadava, G. (2008). An overview on reliability, availability, maintainability and supportability (RAMS) engineering. International Journal of Quality and Reliability Management, 25(3), 330–344.
Storn, R., & Price, K. V. (1995). Differential evolution: A simple and efficient adaptive scheme for global optimization over continuous spaces. Tech. Rep. Technical Report TR-95-012, International Computer Science Institute, Berkley.
Storn, R., & Price, K.V. (1997). Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11(4), 341–359.
Taheri, S., & Zarei, R. (2011). Bayesian system reliability assessment under the vague environment. Applied Soft Computing, 11(2), 1614–1622.
Yeh, W. C., & Hsieh, T. J. (2011). Solving reliability redundancy allocation problems using an artificial bee colony algorithm. Computer and operational research, 38(11), 1465–1473.
Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8, 338–353.
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Garg, H. (2016). Modeling and Analyzing System Failure Behavior for Reliability Analysis Using Soft Computing-Based Techniques. In: Pham, H. (eds) Quality and Reliability Management and Its Applications. Springer Series in Reliability Engineering. Springer, London. https://doi.org/10.1007/978-1-4471-6778-5_4
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