Skip to main content
SpringerLink
Log in

Comparative Analysis of Two Fuzzy Modeling Through Investigation of Condensate System

  • Published:
International Journal of Fuzzy Systems Aims and scope Submit manuscript

A Correction to this article was published on 13 June 2023

This article has been updated

Abstract

The purpose of this paper is to investigate a unit namely Condensate Structure of coal-fired thermal power plant. The data collected may not be exact. There may always be an imprecision due to human errors. To extract the useful information from the records, data have been tackled by tolerance in context of fuzzy logics. To handle the uncertainty in the data, two kinds of tolerances have been used. Fuzzification of data have been done using unilateral tolerance with the membership function as Right triangular generalized fuzzy numbers (RTrGFN) and bilateral tolerance with the membership function as generalized trapezoidal fuzzy numbers (GTFN). Using lambda-tau expressions, failure rate, and repair time for the entire structure has been calculated and all reliability parameters of Condensate structure have been calculated as well. The defuzzified values are also obtained for application purpose. This paper aimed at finding the performance of the structure and best possible way to increase the reliability of the structure. The results have been shown with the help of graphs and tables. Then, ranking has been used to identify the critical component of the system. Comparative study of the considered structure has been made between the results obtained by two different methodologies.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26
Fig. 27

Similar content being viewed by others

Change history

References

  1. Gupta, S.: Stochastic modelling and availability analysis of a critical engineering system. Int. J. Qual. Reliab. Manag. 36(5), 782–796 (2019)

    Article  Google Scholar 

  2. Tanaka, H., Fan, L.T., Lai, F.S., Toguchi, K.: Fault-tree analysis by fuzzy probability. IEEE Trans. Reliab. 32(5), 453–457 (1983)

    Article  MATH  Google Scholar 

  3. Arora, N., Kumar, D.: Availability analysis of steam and power generation systems in the thermal power plant. Microelectron. Reliab. 37(5), 795–799 (1997)

    Article  Google Scholar 

  4. Arora, N., Kumar, D.: Stochastic analysis and maintenance planning of ash handling system in thermal power plant. Microelectron. Reliab. 37(5), 819–834 (2000)

    Article  Google Scholar 

  5. Knezevic, J., Odoom, E.R.: Reliability modelling of repairable systems using Petri nets and fuzzy Lambda-Tau methodology. Reliab. Eng. Syst. Saf. 73(1), 1–17 (2001)

    Article  Google Scholar 

  6. Wang, P., Coit, D.W., et al.: Repairable systems reliability trend tests and evaluation. In: Annual Reliability and Maintainability Symposium. 2005. Proceedings., Alexandria, VA, USA, pp. 416–421 (2005)

  7. Fodor, J., Bede B.: Arithmetics with fuzzy numbers: a comparative overview. Proceeding of 4th Slovakian-Hungarian joints (2006)

  8. Sharma, R.K., Kumar, D., Kumar, P.: Modeling system behavior for risk and reliability analysis using KBARAM. Qual. Reliab. Eng. Int. 23(8), 973–998 (2007)

    Article  Google Scholar 

  9. Saraswat, S., Yadava, G.S.: An overview on reliability, availability, maintainability and supportability (RAMS) engineering. Int. J. Qual. Reliab. Manag. 25(3), 330–344 (2008)

    Article  Google Scholar 

  10. Zio, E.: Reliability engineering: Old problems and new challenges. Reliab. Eng. Syst. Saf. 94(2), 125–141 (2009)

    Article  Google Scholar 

  11. Sharma, R.K., Sharma, P.: Computing RAM indices for reliable operation of production systems. Adv. Prod. Eng. Manag. 7(4), 245–254 (2012)

    Google Scholar 

  12. Garg, H.: Performance and behavior analysis of repairable industrial systems using vague Lambda-Tau methodology. Appl. Soft Comput. 22, 323–328 (2014)

    Article  Google Scholar 

  13. Panchal, D., Kumar, D.: Reliability analysis of CHU system of a coal fired thermal power plant using fuzzy λ-τ approach. In: 12th Global Congress on Manufacturing and Management, Procedia Engineering, Vol. 97, pp. 2323–2332 (2014)

  14. Garg, H.: An approach for analyzing the reliability of industrial system using Fuzzy Kolmogorov’s differential equations. Arab. J. Sci. Eng. 40, 975–987 (2015)

    Article  MATH  Google Scholar 

  15. Panchal, D., Kumar, D.: Stochastic behaviour analysis of power generating unit in thermal power plant using fuzzy methodology. Oper. Res. Soc. India 53(1), 16–40 (2016)

    MATH  Google Scholar 

  16. Dhiman, P., Garg, H.: Reliability analysis of an industrial system using improved arithmetic operations. Master’s thesis. School of Mathematics, Thapar University Patiala, India (2016)

  17. Panchal, D., Kumar, D.: Risk analysis of compressor house unit in thermal power plant using integrated fuzzy FMEA and GRA approach. Int. J. Ind. Syst. Eng. 25(2), 228–250 (2017)

    Google Scholar 

  18. Komal, K.: Fuzzy reliability analysis of the washing system in a paper plant using the TBFLT technique. Int. J. Qual. Reliab. Manag. 34(8), 1352–1372 (2017)

    Article  Google Scholar 

  19. Panchal, D., Srivastva, P.: Qualitative analysis of CNG dispensing system using fuzzy FMEA–GRA integrated approach. Int. J. Syst. Assur. Eng. Manag. 10(1), 44–56 (2018)

    Article  Google Scholar 

  20. Panchal, D., Mangala, S., Tyagi, M., Mange, R.: Risk analysis for clean and sustainable production in a urea fertilizer industry. Int. J. Qual. Reliab. Manag. 35(7), 1459–1476 (2018)

    Article  Google Scholar 

  21. Garg, H.: Analysis of an industrial system under uncertain environment by using different types of fuzzy numbers. Int. J. Syst. Assur. Eng. Manag. 9(2), 525–538 (2018)

    Article  Google Scholar 

  22. Choudhary, D., Tripathi, M., Shankar, R.: Reliability, availability and maintainability analysis of a cement plant: a case study. Int. J. Qual. Reliab. Manag. 36(3), 298–313 (2019)

    Article  Google Scholar 

  23. Panchal, D., Singh, A.K., Chatterjee, P., Zavadskas, E.K., Ghorabaee, M.K.: A new fuzzy methodology-based structured framework for RAM and risk analysis. Appl. Soft Comput. 74, 242–254 (2019)

    Article  Google Scholar 

  24. Dhiman, P., Kumar, A.: RAM assessment of the repairable industrial structure with genuine human-mistake working conditions with generalized fuzzy numbers. Int. J. Qual. Reliab. Manag. 38(7), 1614–1627 (2020)

    Article  Google Scholar 

  25. Tsarouhas, P.: Reliability, availability, and maintainability (RAM) study of an ice cream industry. Appl. Sci. 10(12), 4265 (2020)

    Article  Google Scholar 

  26. Panchal, D., Tyagi, M., Sachdeva, A., Garg, R.K.: Reliability analysis of complex repairable system in thermal power plant. In: Ram, M., Pham, H. (eds.) Advances in Reliability Analysis and its Applications, pp. 361–372. Springer Series in Reliability Engineering, Springer, Cham (2020)

    Chapter  Google Scholar 

  27. Kowal, K., Tarobi, M.: Failure mode and reliability study for electrical facility of the high temperature engineering. Test Reactor. Reliab. Eng. Syst. Saf. 210, 107529 (2021)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Applied Sciences, Symbiosis Institute of Technology, Symbiosis International (Deemed University) (SIU), Lavale, Pune, Maharashtra, 412115, India

    Amit Kumar & Pooja Dhiman

  2. Department of Mathematics, Chitkara University, Rajpura, Punjab, 140401, India

    Amit Kumar & Pooja Dhiman

Authors
  1. Amit Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pooja Dhiman
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Amit Kumar.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kumar, A., Dhiman, P. Comparative Analysis of Two Fuzzy Modeling Through Investigation of Condensate System. Int. J. Fuzzy Syst. 25, 2796–2815 (2023). https://doi.org/10.1007/s40815-023-01532-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40815-023-01532-y

Keywords

Search

Navigation