Skip to main content

Modeling and Analyzing System Failure Behavior for Reliability Analysis Using Soft Computing-Based Techniques

  • Chapter
  • First Online:
Quality and Reliability Management and Its Applications

Part of the book series: Springer Series in Reliability Engineering ((RELIABILITY))

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

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.

    Article  MathSciNet  Google Scholar 

  • Attanassov, K. T. (1989). More on intuitionistic fuzzy sets. Fuzzy Sets and Systems, 33(1), 37–46.

    Article  MathSciNet  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Bustince, H., & Burillo, P. (1996). Vague sets are intuitionistic fuzzy sets. Fuzzy Sets and Systems, 79(3), 403–405.

    Article  MathSciNet  MATH  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Chen, S. M. (2003). Analyzing fuzzy system reliability using vague set theory. International Journal of Applied Science and Engineering, 1(1), 82–88.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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).

    Google Scholar 

  • Garg, H. (2013). Reliability analysis of repairable systems using Petri nets and Vague Lambda-Tau methodology. ISA Transactions, 52(1), 6–18.

    Article  Google Scholar 

  • Garg, H., & Rani, M. (2013). An approach for reliability analysis of industrial systems using PSO and IFS technique. ISA Transactions, 52(6), 701–710.

    Article  Google Scholar 

  • 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).

    Google Scholar 

  • Garg, H., & Sharma, S. P. (2012). Stochastic behavior analysis of industrial systems utilizing uncertain data. ISA Transactions, 51(6), 752–762.

    Google Scholar 

  • Garg, H., & Sharma, S. P. (2013). Multi-objective reliability-redundancy allocation problem using particle swarm optimization. Computers & Industrial Engineering, 64(1), 247–255.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Gau, W. L., & Buehrer, D. J. (1993). Vague sets. IEEE Transaction on Systems, Man, and Cybernetics, 23, 610–613.

    Article  MATH  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Goldberg, D. E. (1989). Genetic algorithm in search, optimization and machine learning. MA: Addison-Wesley.

    MATH  Google Scholar 

  • Holland, J. H. (1975). Adaptation in natural and artificial systems, Ann Arbor. MI: The University of Michigan Press.

    Google Scholar 

  • 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.

    Article  MathSciNet  MATH  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Karaboga, D. (2005). An idea based on honey bee swarm for numerical optimization. Tech. rep., TR06, Erciyes University, Engineering Faculty, Computer Engineering Department.

    Google Scholar 

  • Karaboga, D., & Akay, B. (2009). A comparative study of artificial bee colony algorithm. Applied Mathematics and Computation, 214(1), 108–132.

    Article  MathSciNet  MATH  Google Scholar 

  • 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.

    Article  MathSciNet  MATH  Google Scholar 

  • Karaboga, D., & Ozturk, C. (2011). A novel clustering approach: Artificial bee colony (ABC) algorithm. Applied Soft Computing, 11(1), 652–657.

    Google Scholar 

  • Kennedy, J., & Eberhart, R. C. (1995) Particle swarm optimization. In IEEE International Conference on Neural Networks, Vol. IV (pp. 1942–1948). Piscataway.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Komal, Sharma, S. P., & Kumar, D. (2010). RAM analysis of repairable industrial systems utilizing uncertain data. Applied Soft Computing, 10, 1208–1221.

    Article  Google Scholar 

  • Kuo, W., Prasad, V. R., Tillman, F. A., & Hwang, C. (2001). Optimal reliability design: Fundamentals and applications. Cambridge: Cambridge University Press.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Leou, R. (2006). A method for unit maintenance scheduling considering reliability and operation expense. Electrical Power and Energy Systems, 28, 471–481.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Ross, T. J. (2004). Fuzzy logic with engineering applications, 2nd edn. New York: Wiley.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • Taheri, S., & Zarei, R. (2011). Bayesian system reliability assessment under the vague environment. Applied Soft Computing, 11(2), 1614–1622.

    Article  Google Scholar 

  • 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.

    Article  MathSciNet  Google Scholar 

  • Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8, 338–353.

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Harish Garg .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer-Verlag London

About this chapter

Cite this chapter

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

Download citation

  • DOI: https://doi.org/10.1007/978-1-4471-6778-5_4

  • Published:

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-6776-1

  • Online ISBN: 978-1-4471-6778-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics