Application of a fuzzy inference system for functional failure risk rank estimation: RBM of rotating equipment and instrumentation

Journal of Loss Prevention in the Process Industries

Volumn 29, Issue 1, 2014, Pages 216-224

  Ratnayake, R M Chandima ^a  

^a UNIVERSITY OF STAVANGER   (Norway)

Author keywords

Functional failure risk; Fuzzy inference system; Membership functions; NORSOK Z008; Production and process facilities; Risk based maintenance

Indexed keywords

FUZZY SYSTEMS; INSTRUMENTS; MAINTENANCE; MEMBERSHIP FUNCTIONS; OPTIMIZATION; RISK ASSESSMENT; ROTATING MACHINERY;

FUNCTIONAL FAILURE; FUZZY INFERENCE SYSTEMS; MAINTENANCE ENGINEERS; NORSOK Z008; NORWEGIAN CONTINENTAL SHELVES; OPTIMIZATION APPROACH; RISK-BASED MAINTENANCES; ROTATING EQUIPMENT;

RISK PERCEPTION;

EID: 84898678505     PISSN: 09504230     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jlp.2014.03.002     Document Type: Article

References (42)

1. Amin S., Byington C., Watson M. Fuzzy inference and fusion for health state diagnosis of hydraulic pumps and motors. Annual Meeting of the North American Fuzzy Information Processing Society 2005, 13-18. 10.1109/NAFIPS.2005.1548499.
2. Arunraj N.S., Maiti J. Risk-based maintenance - techniques and applications. Journal of Hazardous Materials 2007, 142(3):653-661.
3. Bertolini M., Bevilacqua M., Ciarapica F.E., Giacchetta G. Development of risk-based inspection and maintenance procedures for an oil refinery. Journal of Loss Prevention in the Process Industries 2009, 22:244-253.
4. Bocaniala C.D., Sa da Costa J., Palade V. Anovel fuzzy classification solution for fault diagnosis. Journal of Intelligent and Fuzzy Systems 2004, 15(3/4):195-205.
5. Bowles J.B., Pelaez C.E. Fuzzy logic prioritization of failures in a system failure mode, effects and criticality analysis. Reliability Engineering & System Safety 1995, 50(2):203-213.
6. Braglia M., Frosolini M., Montanari R. Fuzzy criticality assessment model for failure modes and effects analysis. International Journal of Quality & Reliability Management 2003, 20(4):503-524.
7. Procedures for performing a failure mod, effects and criticality analysis, MIL-STD-1629A 1980, Department of Defense, Washington, DC.
8. Garcia P.A.A., Schirru R., E'Melo P.F. Afuzzy data envelopment analysis approach for FMEA. Progress in Nuclear Energy 2005, 46(3-4):359-373.
9. Guimaraes A.C.F., Lapa C.M.F. Effects analysis fuzzy inference system in nuclear problems using approximate reasoning. Annals of Nuclear Energy 2004, 31(1):107-115.
10. Guo L., Gao J., Yang J., Kang J. Criticality evaluation of petrochemical equipment based on fuzzy comprehensive evaluation and a BP neural network. Journal of Loss Prevention in the Process Industries 2009, 22(4):469-476.
11. Khan F.I., Haddara M.M. Risk-based maintenance (RBM): a quantitative approach for maintenance/inspection scheduling and planning. Journal of Loss Prevention in the Process Industries 2003, 16(6):561-573.
12. Khan F.I., Haddara M.R. Risk-based maintenance of ethylene oxide production facilities. Journal of Hazardous Materials 2004, 108:147-159.
13. Khan K., Sadiq R., Haddara M. Risk-based inspection and maintenance (RBIM): multi-attribute decision making with aggregative risk analysis. Process Safety and Environmental Protection 2004, 82:398-411.
14. Kothamasu R., Huang S.H. Adaptive Mamdani fuzzy model for condition-based maintenance. Fuzzy Sets and Systems 2007, 158(24):2715-2733.
15. Krishnasamy L., Khan F., Haddara M. Development of a risk-based maintenance (RBM) strategy for a power-generating plant. Journal of Loss Prevention in the Process Industries 2005, 18(2):69-81.
16. Kuo R.J. Intelligent diagnosis for turbine blade faults using artificial neural networks and fuzzy logic. Engineering Applications of Artificial Intelligence 1995, 8:25-34.
17. Kwon H.M. The effectiveness of process safety management (PSM) regulation for chemical industry in Korea. Journal of Loss Prevention in the Process Industries 2006, 19:13-16.
18. Li P., Chen G., Daib L., Lib Z. Fuzzy logic-based approach for identifying the risk importance of human error. Safety Science 2010, 48:902-913.
19. Mamdani E.H., Assilian S. An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies 1975, 7(1):1-13.
20. Marhavilas P.K., Koulouriotisb D., Gemenib V. Risk analysis and assessment methodologies in the work sites: on a review, classification and comparative study of the scientific literature of the period 2000-2009. Journal of Loss Prevention in the Process Industries 2011, 24(5):477-523.
21. Masters R. Advanced algorithms for neural networks: A C++ source book 1995, Wiley.
22. Masters T. Practical neural network recipes in C++ 1993, Academic Press.
23. MATLAB-R2013a(8.1.0.604) fuzzy logic toolbox, 1984-2013 2013, MATLAB, The MathWorks, Inc., US.
24. Mili A., Bassettoa S., Siadatb A., Tollenaerea M. Dynamic risk management unveil productivity improvements. Journal of Loss Prevention in the Process Industries 2009, 22(1):25-34.
25. Mure S., Demichela M. Fuzzy Application Procedure (FAP) for the risk assessment of occupational accidents. Journal of Loss Prevention in the Process Industries 2009, 22:593-599.
26. Pinto A. QRAM a qualitative occupational safety risk assessment model for the construction industry that incorporates uncertainties by the use of fuzzy sets. Safety Science 2014, 63:57-76.
27. Regulations relating to conducting petroleum activities (the activities regulations) 2011, Ptil, Accessed on 18.03.13. http://www.ptil.no/activities/category399.html#_Toc345662825.
28. Ratnayake R.M.C. Sustainable asset performance: the role of PAS 55 1&2 and human factors. International Journal of Sustainable Engineering 2012, 6(3):198-211.
29. Ratnayake R.M.C. Adecision model for executing plant strategy: maintaining the technical integrity of petroleum flowlines. International Journal of Decision Sciences, Risk and Management 2012, 4(1/2):1-24.
30. Ratnayake R.M.C. Plant systems and equipment maintenance: use of fuzzy logic for criticality assessment in NORSOK Standard Z008. Proceedings of the IEEE international conference on industrial engineering and engineering management (IEEM) 2013, 978-1-4799-0986-5.
31. Ratnayake R.M.C., Markeset T. Measuring performance for technical integrity management: sustaining abilities of oil and gas operations. Journal of Quality in Maintenance Engineering 2010, 15(1):44-63.
32. Schwartz D.R., Kaufman A.B., Schwartz I.M. Computational intelligence techniques for risk assessment and decision support. Children and Youth Services Review 2004, 26:1081-1095.
33. Seneviratne A.M.N.D., Ratnayake R.M.C. In-service inspection of static mechanical equipment on offshore oil and gas production plants: a decision support framework. Proceedings of the IEEE international conference on industrial engineering and engineering management (IEEM) 2012, 978-1-4673-2945-3.
34. Seneviratne A.M.N.D., Ratnayake R.M.C. In-service inspection of static mechanical equipment: use of a fuzzy inference system for maintaining the quality of an inspection program. Proceedings of the IEEE international conference on industrial engineering and engineering management (IEEM) 2013, 978-1-4799-0986-5.
35. Sharma R.K., Kumar D., Kumar P. Systematic failure mode effect analysis (FMEA) using fuzzy linguistic modelling. International Journal of Quality & Reliability Management 2005, 22(9):986-1004.
36. Sii H.S., Ruxton T., Wanf J. Afuzzy-logic-based approach to qualitative safety modelling for marine systems. Reliability Engineering & System Safety 2001, 73:19-34.
37. Suresh K.R., Mujumdar P.P. Afuzzy risk approach for performance evaluation of an irrigation reservoir system. Agricultural Water Management 2004, 69:159-177.
38. Tay K.M., Lim C.P. On the use of fuzzy inference techniques in assessment models: part II: industrial applications. Fuzzy Optimization and Decision Making 2008, 7(3):283-302.
39. Tomic B. Risk based optimistation of maintenance - methods and approaches. Proceedings of the European safety and reliability conference 1993, 259-265.
40. Wang L.X. Adaptive fuzzy systems and control-Design and stability analysis 1993, University of California at Berkeley, PTR Prentice Hall.
41. Z008 Risk based maintenance and consequence classification 2011, Z008, (Rev. 3, June 2011), Accessed on 08.03.13. http://www.standard.no/no/Fagomrader/Petroleum/NORSOK-Standard-Categories/Z-Regularity-Criticality/Z0082/.
42. Design principles: coding system, Norsok Standard 1996, Z-DP-002, Rev. 2, February 1996.