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Acquiring and Sharing Tacit Knowledge in Failure Diagnosis Analysis Using Intuitionistic and Pythagorean Assessments

  • Mohammad YazdiEmail author
Technical Article---Peer-Reviewed
  • 28 Downloads

Abstract

Nowadays knowledge management has received a considerable attention from both academics and industrial sectors, and expert knowledge is recognized as the most important resource of enterprises, particularly in the knowledge-intensive organizations. Dealing with knowledge creation, transfer, and utilization is increasingly critical for the long-term sustainable competitive advantage and success of any organization. Thus, a lot of efforts have been required from companies and researchers in developing and supporting knowledge management in different organizations. In industrial sectors as the highly competitive environment, capturing and disseminating of tacit knowledge are significant to an organization’s success with the development of knowledge-based systems. Safety and reliability analysis is an important issue to prevent an event which may be the occurrence of catastrophic accident in process industries. In this context, conventional safety and reliability assessment techniques like fault tree analysis have been widely used in this regard; however, in practical knowledge acquisition process, domain experts tend to express their judgments using multi-granularity linguistic term sets, and there usually exists uncertain and incomplete information since expert knowledge is experience-based and tacit. In addition, although the technical capabilities of expert systems based on fuzzy set theory are expanding, they still fall short of meeting the increasingly complex knowledge demands and still suffer in subjective uncertainty processing and dynamic structure representation which are important in risk assessment procedure. In this paper, a new framework based on 2-tuple intuitionistic fuzzy numbers, Pythagorean fuzzy sets, and Bayesian network mechanism is proposed to evaluate system reliability, to deal with mentioned drawbacks, and to recognize the most critical system components which affect the system reliability.

Keywords

Pythagorean Intuitionistic Knowledge management Tacit knowledge Chemical process industry 

Notes

References

  1. 1.
    H.-C. Liu, L. Liu, Q.-L. Lin, N. Liu, Knowledge acquisition and representation using fuzzy evidential reasoning and dynamic adaptive fuzzy Petri nets. IEEE Trans. Cybern. 43, 1059–1072 (2013).  https://doi.org/10.1109/TSMCB.2012.2223671 CrossRefGoogle Scholar
  2. 2.
    D.S. Yeung, E.C.C. Tsang, Fuzzy knowledge representation and reasoning using Petri nets. Expert Syst. Appl. 7, 281–289 (1994).  https://doi.org/10.1016/0957-4174(94)90044-2 CrossRefGoogle Scholar
  3. 3.
    M. Yazdi, The application of Bow–Tie method in hydrogen sulfide risk management using layer of protection analysis (LOPA). J. Fail. Anal. Prev. 17, 291–303 (2017).  https://doi.org/10.1007/s11668-017-0247-x CrossRefGoogle Scholar
  4. 4.
    S. Kabir, An overview of fault tree analysis and its application in model based dependability analysis. Expert Syst. Appl. 77, 114–135 (2017).  https://doi.org/10.1016/j.eswa.2017.01.058 CrossRefGoogle Scholar
  5. 5.
    J.L. Feinstein, Introduction to expert systems. J. Policy Anal. Manag. 8, 182–187 (1989).  https://doi.org/10.2307/3323375 Google Scholar
  6. 6.
    D.S. Yeung, E.C.C. Tsang, Weighted fuzzy production rules. Fuzzy Sets Syst. 88, 299–313 (1997).  https://doi.org/10.1016/S0165-0114(96)00052-8 CrossRefGoogle Scholar
  7. 7.
    C.G. Looney, Fuzzy Petri nets for rule-based decision making. IEEE Trans. Syst. Man. Cybern. 18, 178–183 (1988).  https://doi.org/10.1109/21.87067 CrossRefGoogle Scholar
  8. 8.
    M. Polanyi, The Tacit Dimension (1966). https://philpapers.org/rec/POLTTD-2. (Accessed March 8, 2018)
  9. 9.
    H. Li, J.-X. You, H.-C. Liu, G. Tian, Acquiring and sharing tacit knowledge based on interval 2-Tuple linguistic assessments and extended fuzzy Petri nets. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 26, 43–65 (2018).  https://doi.org/10.1142/s0218488518500034 CrossRefGoogle Scholar
  10. 10.
    H.-C. Liu, Q.-L. Lin, L.-X. Mao, Z.-Y. Zhang, Dynamic adaptive fuzzy Petri nets for knowledge representation and reasoning. IEEE Trans. Syst. Man. Cybern. Syst. 43, 1399–1410 (2013).  https://doi.org/10.1109/tsmc.2013.2256125 Google Scholar
  11. 11.
    M. Yazdi, An extension of Fuzzy Improved Risk Graph and Fuzzy Analytical Hierarchy Process for determination of chemical complex safety integrity levels. Int. J. Occup. Saf. Ergon. (2017).  https://doi.org/10.1080/10803548.2017.1419654 Google Scholar
  12. 12.
    K.-Q. Zhou, A.M. Zain, Fuzzy Petri nets and industrial applications: a review. Artif. Intell. Rev. 45, 405–446 (2016).  https://doi.org/10.1007/s10462-015-9451-9 CrossRefGoogle Scholar
  13. 13.
    X. Deng, D. Han, J. Dezert, Y. Deng, Y. Shyr, Evidence combination from an evolutionary game theory perspective. IEEE Trans. Cybern. 46, 2070–2082 (2016)CrossRefGoogle Scholar
  14. 14.
    H.S. Yan, A new complicated-knowledge representation approach based on knowledge meshes. IEEE Trans. Knowl. Data Eng. 18, 47–62 (2006).  https://doi.org/10.1109/tkde.2006.2 CrossRefGoogle Scholar
  15. 15.
    L. Zadeh, Fuzzy sets. Inf. Control 8, 338–353 (1965)CrossRefGoogle Scholar
  16. 16.
    M. Yazdi, M. Darvishmotevali, Fuzzy-Based Failure Diagnostic Analysis in a Chemical Process Industry (Springer, Cham, 2019), pp. 724–731.  https://doi.org/10.1007/978-3-030-04164-9_95 Google Scholar
  17. 17.
    C.S. Liaw, Y.C. Chang, K.H. Chang, T.Y. Chang, ME-OWA based DEMATEL reliability apportionment method. Expert Syst. Appl. 38, 9713–9723 (2011).  https://doi.org/10.1016/j.eswa.2011.02.029 CrossRefGoogle Scholar
  18. 18.
    H.C. Liu, J.X. You, X.Y. You, Evaluating the risk of healthcare failure modes using interval 2-tuple hybrid weighted distance measure. Comput. Ind. Eng. 78, 249–258 (2014).  https://doi.org/10.1016/j.cie.2014.07.018 CrossRefGoogle Scholar
  19. 19.
    S. Wan, G. Xu, J. Dong, Supplier selection using ANP and ELECTRE II in interval 2-tuple linguistic environment. Inf. Sci. (Ny). 385–386, 19–38 (2017).  https://doi.org/10.1016/j.ins.2016.12.032 CrossRefGoogle Scholar
  20. 20.
    A. Singh, A. Gupta, A. Mehra, Energy planning problems with interval-valued 2-tuple linguistic information. Oper. Res. 17, 821–848 (2017).  https://doi.org/10.1007/s12351-016-0245-x Google Scholar
  21. 21.
    H.C. Liu, M.L. Ren, J. Wu, Q.L. Lin, An interval 2-tuple linguistic MCDM method for robot evaluation and selection. Int. J. Prod. Res. 52, 2867–2880 (2014).  https://doi.org/10.1080/00207543.2013.854939 CrossRefGoogle Scholar
  22. 22.
    M.M. Shan, J.X. You, H.C. Liu, Some interval 2-tuple linguistic harmonic mean operators and their application in material selection. Adv. Mater. Sci. Eng. (2016).  https://doi.org/10.1155/2016/7034938 Google Scholar
  23. 23.
    J. Lin, Q. Zhang, F. Meng, An approach for facility location selection based on optimal aggregation operator. Knowl. Based Syst. 85, 143–158 (2015).  https://doi.org/10.1016/j.knosys.2015.05.001 CrossRefGoogle Scholar
  24. 24.
    E. Bozdag, U. Asan, A. Soyer, S. Serdarasan, Risk prioritization in Failure mode and effects analysis using interval type-2 fuzzy sets. Expert Syst. Appl. 42, 4000–4015 (2015).  https://doi.org/10.1016/j.eswa.2015.01.015 CrossRefGoogle Scholar
  25. 25.
    H. Liu, L. Liu, P. Li, Failure mode and effects analysis using intuitionistic fuzzy hybrid weighted Euclidean distance operator. Int. J. Syst. Sci. 45, 2012–2030 (2014).  https://doi.org/10.1080/00207721.2012.760669 CrossRefGoogle Scholar
  26. 26.
    M. Yazdi, Risk assessment based on novel intuitionistic fuzzy-hybrid-modified TOPSIS approach. Saf. Sci. 110, 438–448 (2018).  https://doi.org/10.1016/j.ssci.2018.03.005 CrossRefGoogle Scholar
  27. 27.
    M. Yazdi, H. Soltanali, Knowledge acquisition development in failure diagnosis analysis as an interactive approach. J. Interact. Des. Manuf. Int. (2018).  https://doi.org/10.1007/s12008-018-0504-6 Google Scholar
  28. 28.
    M. Yazdi, Footprint of knowledge acquisition improvement in failure diagnosis analysis. Qual. Reliab. Eng. Int. 35, 405–422 (2018).  https://doi.org/10.1002/qre.2408 CrossRefGoogle Scholar
  29. 29.
    S. Rajakarunakaran, A. Maniram Kumar, V. Arumuga Prabhu, Applications of fuzzy faulty tree analysis and expert elicitation for evaluation of risks in LPG refuelling station. J. Loss Prev. Process Ind. 33, 109–123 (2015).  https://doi.org/10.1016/j.jlp.2014.11.016 CrossRefGoogle Scholar
  30. 30.
    M. Yazdi, E. Zarei, Uncertainty handling in the safety risk analysis: an integrated approach based on fuzzy fault tree analysis. J. Fail. Anal. Prev. (2018).  https://doi.org/10.1007/s11668-018-0421-9 Google Scholar
  31. 31.
    S. Kabir, M. Yazdi, J.I. Aizpurua, Y. Papadopoulos, Uncertainty-Aware dynamic reliability analysis framework for complex systems. IEEE Access. 6, 29499–29515 (2018).  https://doi.org/10.1109/ACCESS.2018.2843166 CrossRefGoogle Scholar
  32. 32.
    S. Ming-Hung, C. Ching-Hsue, J.-R. Chang, Using intuitionistic fuzzy sets for fault-tree analysis on printed circuit board. Assembly 46, 2139–2148 (2006).  https://doi.org/10.1016/j.microrel.2006.01.007 Google Scholar
  33. 33.
    J.R. Chang, K.H. Chang, S.H. Liao, C.H. Cheng, The reliability of general vague fault-tree analysis on weapon systems fault diagnosis. Soft. Comput. 10, 531–542 (2006).  https://doi.org/10.1007/s00500-005-0483-y CrossRefGoogle Scholar
  34. 34.
    S.R. Cheng, B. Lin, B.M. Hsu, M.H. Shu, Fault-tree analysis for liquefied natural gas terminal emergency shutdown system. Expert Syst. Appl. 36, 11918–11924 (2009).  https://doi.org/10.1016/j.eswa.2009.04.011 CrossRefGoogle Scholar
  35. 35.
    M. Kumar, S.P. Yadav, The weakest t -norm based intuitionistic fuzzy fault-tree analysis to evaluate system reliability. ISA Trans. 51, 531–538 (2012).  https://doi.org/10.1016/j.isatra.2012.01.004 CrossRefGoogle Scholar
  36. 36.
    M. Gul, Application of Pythagorean fuzzy AHP and VIKOR methods in occupational health and safety risk assessment: the case of a gun and rifle barrel external surface oxidation and colouring unit. Int. J. Occup. Saf. Ergon. (2018).  https://doi.org/10.1080/10803548.2018.1492251 Google Scholar
  37. 37.
    E. Ilbahar, A. Karaşan, S. Cebi, C. Kahraman, A novel approach to risk assessment for occupational health and safety using Pythagorean fuzzy AHP & fuzzy inference system. Saf. Sci. 103, 124–136 (2018).  https://doi.org/10.1016/J.SSCI.2017.10.025 CrossRefGoogle Scholar
  38. 38.
    A. Karasan, E. Ilbahar, S. Cebi, C. Kahraman, A new risk assessment approach: safety and critical effect analysis (SCEA) and its extension with Pythagorean fuzzy sets. Saf. Sci. 108, 173–187 (2018).  https://doi.org/10.1016/J.SSCI.2018.04.031 CrossRefGoogle Scholar
  39. 39.
    N.E. Oz, S. Mete, F. Serin, M. Gul, Risk assessment for clearing and grading process of a natural gas pipeline project: An extended TOPSIS model with Pythagorean fuzzy sets for prioritizing hazards. Hum. Ecol. Risk Assess. (2018).  https://doi.org/10.1080/10807039.2018.1495057 Google Scholar
  40. 40.
    R. Abbassi, J. Bhandari, F. Khan, V. Garaniya, S. Chai, Developing a quantitative risk-based methodology for maintenance scheduling using Bayesian network. Chem. Eng. Trans. 48, 235–240 (2016).  https://doi.org/10.3303/CET1648040 Google Scholar
  41. 41.
    M. Abimbola, F. Khan, N. Khakzad, Dynamic safety risk analysis of offshore drilling. J. Loss Prev. Process Ind. 30, 74–85 (2014).  https://doi.org/10.1016/j.jlp.2014.05.002 CrossRefGoogle Scholar
  42. 42.
    T.D. Nielsen, F.V. Jensen, Bayesian networks and decision graphs, vol. 2nd (Springer, New York, 2009)Google Scholar
  43. 43.
    E. Zarei, A. Azadeh, M.M. Aliabadi, I. Mohammadfam, Dynamic safety risk modeling of process systems using Bayesian network. Process Saf. Prog. 36, 399–407 (2017).  https://doi.org/10.1002/prs.11889 CrossRefGoogle Scholar
  44. 44.
    M. Yazdi, A review paper to examine the validity of Bayesian network to build rational consensus in subjective probabilistic failure analysis. Int. J. Syst. Assur. Eng. Manag. (2019).  https://doi.org/10.1007/s13198-018-00757-7 Google Scholar
  45. 45.
    M. Yazdi, S. Kabir, Fuzzy evidence theory and Bayesian networks for process systems risk analysis. Hum. Ecol. Risk Assess. (2019).  https://doi.org/10.1080/10807039.2018.1493679 Google Scholar
  46. 46.
    S. Kabir, M. Walker, Y. Papadopoulos, Dynamic system safety analysis in HiP-HOPS with Petri nets and Bayesian networks. Saf. Sci. 105, 55–70 (2018).  https://doi.org/10.1016/j.ssci.2018.02.001 CrossRefGoogle Scholar
  47. 47.
    M. Yazdi, F. Nikfar, M. Nasrabadi, Failure probability analysis by employing fuzzy fault tree analysis. Int. J. Syst. Assur. Eng. Manag. 8, 1177–1193 (2017).  https://doi.org/10.1007/s13198-017-0583-y CrossRefGoogle Scholar
  48. 48.
    M. Yazdi, O. Korhan, S. Daneshvar, Application of fuzzy fault tree analysis based on modified fuzzy AHP and fuzzy TOPSIS for fire and explosion in process industry. Int. J. Occup. Saf. Ergon. (2018).  https://doi.org/10.1080/10803548.2018.1454636 Google Scholar
  49. 49.
    A.K. Verma, A. Srividya, D.R. Karanki, Reliability and Safety Engineering (Springer, London, 2010).  https://doi.org/10.1007/978-1-84996-232-2 CrossRefGoogle Scholar
  50. 50.
    F.V. Jensen, T.D. Nielsen, Bayesian Networks and Decision Graphs (Springer, Berlin, 2007).  https://doi.org/10.1007/978-0-387-68282-2 CrossRefGoogle Scholar
  51. 51.
    D.N. Ford, J.D. Sterman, Expert knowledge elicitation to improve formal and mental models. Syst. Dyn. Rev. 14, 309–340 (1998).  https://doi.org/10.1002/(SICI)1099-1727(199824)14:4%3c309:AID-SDR154%3e3.0.CO;2-5 CrossRefGoogle Scholar
  52. 52.
    M. Yazdi, S. Daneshvar, H. Setareh, An extension to fuzzy developed failure mode and effects analysis (FDFMEA) application for aircraft landing system. Saf. Sci. 98, 113–123 (2017).  https://doi.org/10.1016/j.ssci.2017.06.009 CrossRefGoogle Scholar
  53. 53.
    S. Helvacioglu, E. Ozen, Fuzzy based failure modes and effect analysis for yacht system design. Ocean Eng. 79, 131–141 (2014).  https://doi.org/10.1016/j.oceaneng.2013.12.015 CrossRefGoogle Scholar
  54. 54.
    T.L. Saaty, A scaling method for priorities in hierarchical structures. J. Math. Psychol. 15, 234–281 (1977).  https://doi.org/10.1016/0022-2496(77)90033-5 CrossRefGoogle Scholar
  55. 55.
    A.F. Guneri, M. Gul, S. Ozgurler, A fuzzy AHP methodology for selection of risk assessment methods in occupational safety. Int. J. Risk Assess. Manag. 18, 319 (2015).  https://doi.org/10.1504/IJRAM.2015.071222 CrossRefGoogle Scholar
  56. 56.
    M. Yazdi, S. Kabir, A fuzzy Bayesian network approach for risk analysis in process industries. Process Saf. Environ. Prot. 111, 507–519 (2017).  https://doi.org/10.1016/j.psep.2017.08.015 CrossRefGoogle Scholar
  57. 57.
    J.J. Buckley, Fuzzy hierarchical analysis. Fuzzy Sets Syst. 17, 233–247 (1985).  https://doi.org/10.1016/0165-0114(85)90090-9 CrossRefGoogle Scholar
  58. 58.
    D.-Y. Chang, Applications of the extent analysis method on fuzzy AHP. Eur. J. Oper. Res. 95, 649–655 (1996).  https://doi.org/10.1016/0377-2217(95)00300-2 CrossRefGoogle Scholar
  59. 59.
    M. Yazdi, Improving failure mode and effect analysis (FMEA) with consideration of uncertainty handling as an interactive approach. Int. J. Interact. Des. Manuf. (2018).  https://doi.org/10.1007/s12008-018-0496-2 Google Scholar
  60. 60.
    K.T. Atanassov, Intuitionistic fuzzy sets. Fuzzy Sets Syst. 20, 87–96 (1986).  https://doi.org/10.1016/S0165-0114(86)80034-3 CrossRefGoogle Scholar
  61. 61.
    Z. Xu, Approaches to multiple attribute group decision making based on intuitionistic fuzzy power aggregation operators. Knowledge-Based Syst. 24, 749–760 (2011).  https://doi.org/10.1016/j.knosys.2011.01.011 CrossRefGoogle Scholar
  62. 62.
    K.H. Chang, C.H. Cheng, Y.C. Chang, Reprioritization of failures in a silane supply system using an intuitionistic fuzzy set ranking technique. Soft. Comput. 14, 285–298 (2010).  https://doi.org/10.1007/s00500-009-0403-7 CrossRefGoogle Scholar
  63. 63.
    K.-H. Chang, C.-H. Cheng, A risk assessment methodology using intuitionistic fuzzy set in FMEA. Int. J. Syst. Sci. 41, 1457–1471 (2010).  https://doi.org/10.1080/00207720903353633 CrossRefGoogle Scholar
  64. 64.
    E. Szmidt, Distances and similarities in intuitionistic fuzzy sets (Springer, Cham, 2014).  https://doi.org/10.1007/978-3-319-01640-5_1 CrossRefGoogle Scholar
  65. 65.
    Z. Xu, R.R. Yager, Some geometric aggregation operators based on intuitionistic fuzzy sets. Int. J. Gen Syst 35, 417–433 (2006).  https://doi.org/10.1080/03081070600574353 CrossRefGoogle Scholar
  66. 66.
    W. Wang, X. Liu, Intuitionistic fuzzy information aggregation using Einstein operations. IEEE Trans. Fuzzy Syst. 20, 923–938 (2012).  https://doi.org/10.1109/TFUZZ.2012.2189405 CrossRefGoogle Scholar
  67. 67.
    Z. Xu, N. Zhao, Information fusion for intuitionistic fuzzy decision making: an overview. Inf. Fus. 28, 10–23 (2016).  https://doi.org/10.1016/j.inffus.2015.07.001 CrossRefGoogle Scholar
  68. 68.
    Z. Xu, Intuitionistic fuzzy aggregation operators. IEEE Trans. Fuzzy Syst. 15, 1179–1187 (2007)CrossRefGoogle Scholar
  69. 69.
    S. Zeng, The intuitionistic fuzzy ordered weighted averaging-weighted average operator and its application in financial decision making. World Acad. Sci. Eng. Technol. 6, 541–547 (2012)Google Scholar
  70. 70.
    F.E. Boran, S. Genç, M. Kurt, D. Akay, A multi-criteria intuitionistic fuzzy group decision making for supplier selection with TOPSIS method. Expert Syst. Appl. 36, 11363–11368 (2009).  https://doi.org/10.1016/j.eswa.2009.03.039 CrossRefGoogle Scholar
  71. 71.
    M. Yazdi, A. Nedjati, R. Abbassi, Fuzzy dynamic risk-based maintenance investment optimization for offshore process facilities. J. Loss Prev. Process Ind. 57, 194–207 (2019).  https://doi.org/10.1016/j.jlp.2018.11.014 CrossRefGoogle Scholar
  72. 72.
    D. Huang, T. Chen, M.-J.J. Wang, A fuzzy set approach for event tree analysis. Fuzzy Sets Syst. 118, 153–165 (2001).  https://doi.org/10.1016/S0165-0114(98)00288-7 CrossRefGoogle Scholar
  73. 73.
    R.R. Yager, Pythagorean fuzzy subsets, in 2013 Joint IFSA World Congress and NAFIPS Annual Meeting, IEEE, pp. 57–61 (2013).  https://doi.org/10.1109/ifsa-nafips.2013.6608375
  74. 74.
    R.R. Yager, Pythagorean membership grades in multicriteria decision making. IEEE Trans. Fuzzy Syst. 22, 958–965 (2014).  https://doi.org/10.1109/TFUZZ.2013.2278989 CrossRefGoogle Scholar
  75. 75.
    R.R. Yager, A.M. Abbasov, Pythagorean membership grades, complex numbers, and decision making. Int. J. Intell. Syst. 28, 436–452 (2013).  https://doi.org/10.1002/int.21584 CrossRefGoogle Scholar
  76. 76.
    T. Onisawa, An approach to human reliability in man-machine systems using error possibility. Fuzzy Sets Syst. 27, 87–103 (1988).  https://doi.org/10.1016/0165-0114(88)90140-6 CrossRefGoogle Scholar
  77. 77.
    M. Yazdi, Hybrid probabilistic risk assessment using fuzzy FTA and fuzzy AHP in a process industry. J. Fail. Anal. Prev. 17, 756–764 (2017).  https://doi.org/10.1007/s11668-017-0305-4 CrossRefGoogle Scholar

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© ASM International 2019

Authors and Affiliations

  1. 1.Instituto Superior TécnicoUniversidade de LisboaLisbonPortugal

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