Advertisement

Human Reliability Analysis Technique Selection for Life Support Systems Maintenance of Orbital Space Stations Using Fuzzy AHP and ANN

Conference paper
  • 792 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 778)

Abstract

A methodology for assessment of a suitable Human Reliability Analysis (HRA) technique for Orbital Space Station (OSS) Environment Control and Life Support System (ECLSS) maintenance considering the ones proposed from other industries is presented. This paper presents a hybrid model using Fuzzy analytic hierarchy process (Fuzzy AHP) and artificial neural networks (ANNs) theory to develop a suitable HRA methodology for OSS ECLSS maintenance. The model consists of two modules: Module 1 applies Fuzzy AHP using pair wise comparison of criteria for existing methods as proposed from other industrial domains. Four criteria that are decided are adequacy, costs, effectiveness and efficacy. The four HRA methodologies that are considered to be evaluated are THERP, CREAM, NARA and SPAR-H. Module 2 utilizes the results of Fuzzy AHP decision matrix into Artificial Neural Network (ANN) model for ranking of all selected HRA methodologies. The results yield the best HRA model for OSS ECLSS maintenance with appropriate scores to compare the performance of each HRA model.

Keywords

Human Reliability Analysis (HRA) Environmental Control and Life Support System (ECLSS) Fuzzy AHP Artificial Neural Network (ANN) THERP CREAM NARA SPAR-H 

References

  1. 1.
    Dhillon, D.S.: Human Reliability and Error in Transportation Systems. Springer, London (2007)Google Scholar
  2. 2.
    Dhillon, D.S.: Human Reliability, Error, and Human Factors in Power Generation. Springer, Cham (2014)CrossRefGoogle Scholar
  3. 3.
    Bell, J., Holroyd, J.: Review of Human Reliability Assessment Methods. Health and Safety Laboratory Harpur Hill, Buxton (2009)Google Scholar
  4. 4.
    Yang, Y., Liu, W., Kang, R., Zheng, W.: The model framework of human reliability for complicated spaceflight mission. In: IEEE Prognostics & System Health Management (PHM) Conference, Beijing (2012).  https://doi.org/10.1109/phm.2012.6228776
  5. 5.
    Hallbert, B., Kolaczkowski, A.: The Employment of Empirical Data and Bayesian Methods in Human Reliability Analysis: A Feasibility Study. Idaho National Laboratory, Idaho Falls (2007)Google Scholar
  6. 6.
    Swain, A.D., Guttmann, H.E.: Handbook of Human Reliability Analysis with Emphasis on Nuclear Power Plant Applications. Sandia National Laboratories Albuquerque, New Mexico and Livermore, California (1983)Google Scholar
  7. 7.
    Stamatelatos, M., Dezfuli, H.: Probabilistic Risk Assessment Procedures Guide for NASA Managers and Practitioners. NASA Scientific and Technical (STI) Program (2011)Google Scholar
  8. 8.
    Boring, R.L.: Fifty Years of THERP and Human Reliability Analysis. Idaho National Laboratory Idaho Falls, Idaho (2012)Google Scholar
  9. 9.
    Gertman, D., Blackman, H., et al.: The SPAR-H Human Reliability Analysis Method. Idaho National Laboratory Battelle Energy Alliance, Idaho Falls, Idaho (2005)Google Scholar
  10. 10.
    Whaley, A.M., Kelly, D.L., et al.: SPAR-H Step-by-Step Guidance. Idaho National Laboratory Risk, Reliability, and NRC Programs Department, Idaho Falls, Idaho (2011)Google Scholar
  11. 11.
    Petruni, A., Giagloglou, E., et. al.: Applying analytic hierarchy process (AHP) to choose a human factor technique: choosing the suitable human reliability analysis technique for the automotive industry. J. Saf. Sci. (2017). http://dx.doi.org/6/j.ssci.2017.05.007
  12. 12.
    Tang, Y., Thomas, L.: Application of the fuzzy analytic hierarchy process to the lead-free equipment selection decision. Int. J. Bus. Syst. Res. 5, 35–56 (2011)CrossRefGoogle Scholar
  13. 13.
    Celik, M., Deha, I., Ozok, A.: Application of fuzzy extended AHP methodology on shipping registry selection: the case of Turkish maritime industry. J. Expert Syst. Appl. 36, 190–198 (2009)CrossRefGoogle Scholar
  14. 14.
    Wang, Y., Luo, Y., et al.: On the extent analysis method for fuzzy AHP and its applications. Eur. J. Oper. Res. 186, 735–747 (2008)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Chang, D.: Applications of the extent analysis method on fuzzy AHP. Eur. J. Oper. Res. 95, 649–655 (1996)CrossRefGoogle Scholar
  16. 16.
    Zhu, K., Jing, Y., et al.: On discussion on extent analysis method and applications of fuzzy AHP. Eur. J. Oper. Res. 116, 450–456 (1999)CrossRefGoogle Scholar
  17. 17.
    Taha, Z., Rostam, S.: A fuzzy AHP–ANN-based decision support system for machine tool selection in a flexible manufacturing cell. Int. J. Adv. Manuf. Technol. 57, 719–733 (2011)CrossRefGoogle Scholar
  18. 18.
    Rajpal, P.S., Shishodia, K.S., et al.: An artificial neural network for modeling reliability, availability and maintainability of a repairable system. Reliabil. Eng. Syst. Saf. 91, 809–819 (2006)CrossRefGoogle Scholar
  19. 19.
    Kumar, J., Roy, N.: A hybrid method for vendor selection using neural network. Int. J. Comput. App. 11, 35–40 (2010)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Subir Chowdhury School of Quality and ReliabilityIndian Institute of Technology KharagpurKharagpurIndia

Personalised recommendations