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Performance analysis of an industrial system using soft computing based hybridized technique

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Journal of the Brazilian Society of Mechanical Sciences and Engineering Aims and scope Submit manuscript

Abstract

Due to imprecise information, it is always difficult for the system analyst to predict and enhance the performance of the system up to the desired degree of accuracy. Therefore, the main task is to reduce the uncertainty level for decision makers, so as to take a more sound decision in a reasonable time. For handling these issues, this paper addressed the various reliability parameters of the industrial system, which depicts the behavior of the system, by quantifying the uncertainties in the data in the form of fuzzy numbers. The corresponding membership functions of the system’s parameters are computed by formulating a nonlinear optimization model and solve it. The obtained results were compared with the existing as well as traditional methodology and results and found that they had less range of uncertainties during the analysis. A sensitivity as well as performance analysis has also been done for depicting the most critical component of the system. Finally, an approach has been illustrated through a case study of cattle feed plant, a repairable industrial system.

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References

  1. Cai KY (1996) System failure engineering and fuzzy methodology: an introductory overview. Fuzzy Sets Syst 83:113–133

    Article  Google Scholar 

  2. Chen K, Ma C, Zheng M, Gao F (2015) Optimization and mechanical accuracy reliability of a new type of forging manipulator. Chin J Mech Eng 28(2):236–248

    Article  Google Scholar 

  3. Cheng CH, Mon DL (1993) Fuzzy system reliability analysis by interval of confidence. Fuzzy Sets Syst 56(1):29–35

    Article  Google Scholar 

  4. Garg H (2013) Reliability analysis of repairable systems using Petri nets and Vague Lambda–Tau methodology. ISA Trans 52(1):6–18

    Article  Google Scholar 

  5. Garg H (2015) A Hybrid GA—GSA algorithm for optimizing the performance of an industrial system by utilizing uncertain data. Handbook of Research on Artificial Intelligence Techniques and Algorithms, IGI Global, pp 620–654. doi:10.4018/978-1-4666-7258-1.ch020

  6. Garg H (2016) A new generalized improved score function of interval-valued intuitionistic fuzzy sets and applications in expert systems. Appl Soft Comput 38:988–999

    Article  Google Scholar 

  7. Garg H (2016) A novel approach for analyzing the reliability of series-parallel system using credibility theory and different types of intuitionistic fuzzy numbers. J Braz Soc Mech Sci Eng 38(3):1021–1035

    Article  Google Scholar 

  8. Garg H, Rani M, Sharma SP (2014) An approach for analyzing the reliability of industrial systems using soft computing based technique. Expert Syst Appl 41(2):489–501

    Article  Google Scholar 

  9. Garg H, Rani M, Sharma SP, Vishwakarma Y (2014) Bi-objective optimization of the reliability-redundancy allocation problem for series-parallel system. J Manuf Syst 33(3):335–347

    Article  Google Scholar 

  10. Garg H, Sharma SP (2011) Behavior and system performance optimization for an industrial system by using particle swarm optimization. In: 2011 IEEE International Conference on Intelligent Computing and Intelligent Systems (ICIS 2011), Guangzhou, China, pp 237–241

  11. Garg H, Sharma SP (2012) Stochastic behavior analysis of industrial systems utilizing uncertain data. ISA Trans 51(6):752–762

    Article  Google Scholar 

  12. Garg H, Sharma SP, Rani M (2012) Stochastic behavior analysis of an industrial systems using PSOBLT technique. Int J Uncertain Fuzziness Knowl Based Syst 20(05):741–761

    Article  Google Scholar 

  13. Knezevic J, Odoom ER (2001) Reliability modeling of repairable systems using Petri nets and Fuzzy Lambda–Tau methodology. Reliab Eng Syst Saf 73(1):1–17

    Article  Google Scholar 

  14. Komal Sharma SP, Kumar D (2009) Stochastic behavior analysis of the press unit in a paper mill using GABLT technique. Int J Intell Comput Cybern 2(3):574–593

    Article  MathSciNet  Google Scholar 

  15. Li C, Zhang Y, Xu M (2012) Reliability-based maintenance optimization under imperfect predictive maintenance. Chin J Mech Eng 25(1):160–165

    Article  Google Scholar 

  16. Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353

    Article  MATH  Google Scholar 

  17. Zimmermann HJ (2001) Fuzzy set theory and its applications, 4th edn, Springer

Download references

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Authors and Affiliations

  1. School of Mathematics, Thapar University Patiala, Patiala, 147004, Punjab, India

    Harish Garg

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  1. Harish Garg
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Correspondence to Harish Garg.

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Technical Editor: Marcelo A. Trindade.

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Garg, H. Performance analysis of an industrial system using soft computing based hybridized technique. J Braz. Soc. Mech. Sci. Eng. 39, 1441–1451 (2017). https://doi.org/10.1007/s40430-016-0552-4

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  • DOI: https://doi.org/10.1007/s40430-016-0552-4

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