Fuzzy-FMEA risk evaluation approach for LHD machine - a case study

• Autorzy: Balaraju Jakkula , Raj Mandela Govinda , Murthy Chivukula Suryanarayana

Identyfikatory

DOI

10.46873/2300-3960.1111

• Języki publikacji: EN

Abstrakty

EN

Improvement of productivity has become an important goal for mining industries in order to meet the expected targets of production and increased price competitiveness. Productivity can be improved in different ways. The effective utilization of men and machinery is one such way. Equipment is prone to numerous unexpected potential failures during its operation. Failure Mode and Effect Analysis (FMEA) is one of the suitable techniques of reliability modeling used to investigate the failure behavior of a complex system. In conventional FMEA, the risk level of failures, a ranking of failures and prioritization of necessary actions is made on the basis of estimated Risk Priority Number (RPN). While this approach is easy and uncomplicated, there are a few flaws in acquiring the best approximation of the failure. The estimation of RPN is made by multiplying the Severity (S), Occurrence (O) and Detection (D) alone and irrespective of the degree of importance of each input. Hence, a new risk management approach known as the Fuzzy rule base interface system was proposed in this research in order to mitigate the failures. Fuzzy FMEA is designed in order to acquire the highest Fuzzy RPN value which will be used as the focus of enhancements to reduce the probability of occurrence of some kind of failure for a second time. This study focused on the Root Cause Analysis (RCA) of underground mining machinery such as Load-Haul-Dumper (LHD). 16 potential risks of various sub-system breakdowns were identified in Fuzzy FMEA. The highest value of RPN 168 (for potential failure mode-F9) was obtained for the electrical subsystem (SSE), as was the highest FRPN 117 (F9). There is a difference between the RPN and FRPN values. The FRPN value is obtained from Fuzzy field generation with consideration of the degree of importance of the given input data. In addition, the recommendations were made based on the analysis to reduce the uneven occurrence of failures.

Słowa kluczowe

EN

risk; failure; FMEA; RPN; LHD

PL

ryzyko; awaria; FMEA; RPN; LHD

• Wydawca: Elsevier ; Główny Instytut Górnictwa
• Czasopismo: Journal of Sustainable Mining
• Rocznik: 2019
• Tom: Vol. 18, iss. 4
• Strony: 257--268
• Opis fizyczny: Bibliogr. 30 poz.

Twórcy

autor

Balaraju Jakkula

• Department of Mining Engineering, NITK Surathkal, Karnataka, 575 025, India

autor

Raj Mandela Govinda

autor

Murthy Chivukula Suryanarayana

• Department of Mining Engineering, NITK Surathkal, Karnataka, 575 025, India

Bibliografia

• 1. Ahsen, A. V. (2008). Cost-oriented failure mode and effects analysis. International Journal of Quality & Reliability Management, 25(5), 466-476. https://doi.org/10.1108/02656710873871.
• 2. Arvanitoyannis, I. S., & Varzakas, T. H. (2009). Application of failure mode and effect analysis (FMEA) and cause and effect analysis for industrial processing of common octopus (Octopus vulgaris) - Part II. International Journal of Food Science and Technology, 44(1), 79-92. https://doi.org/10.1111/j.1365-2621.2007.01640.x.
• 3. Bloom, N. B. (2006). Reliability centered maintenance - implementation made simple. New York: McGraw-Hill Part 5, page 3. BS5760. British Standards Institution.
• 4. Bowles, J. B., & Pelaez, C. E. (1995). Application of fuzzy logic to reliability engineering. Proceedings of the IEEE, 83(3), 435-449.
• 5. Braaksma, A. J. J., Klingenberg, W., & Veldman, J. (2013). Failure mode and effect analysis in asset maintenance: A multiple case study in the process industry. International Journal of Production Research, 51(4), 1055-1071. https://doi.org/10.1080/00207543.2012.674648.
• 6. Bubbico, R., Cave, S. Di, & Mazzarotta, B. (2004). Risk analysis for road and rail transport of hazardous materials: A simplified approach. Journal of Loss Prevention in the Process Industries, 17, 477-482. https://doi.org/10.1016/j.jlp.2004.08.010.
• 7. Chen, S. H. (1985). Ranking fuzzy numbers with maximizing and minimizing set. Fuzzy Sets and Systems, 17(2), 113-129.
• 8. Chin, K. S., Chan, A., & Yang, J. B. (2008). Development of a fuzzy FMEA based product design system. International Journal of Advanced Manufacturing Technology, 36(7-8), 633-649.
• 9. Gargama, H., & Chaturvedi, S. K. (2011). Criticality assessment models for failure mode effects and criticality analysis using fuzzy logic. IEEE Transactions on Reliability, 60(1), 102-110.
• 10. Keskin, G.-A., & Ozkan, C. (2009). An alternative evaluation of FMEA: Fuzzy ART algorithm. Quality and Reliability Engineering International, 25, 647-661.
• 11. Kusumadewi, S. (2002). Analisis dan desain sistem fuzzy menggunakan toolbox matlab. Yogyakarta: Graha Ilmu979-3289-02-3 Daftar Pustaka: hlm.
• 12. Maiti, J. (2005). The Basics of Risk Assessment, Conference on Technological advancements and Environmental challenges in mining and allied industries in the 21st century. NIT Rourkela.
• 13. Mobley, R. K., & Smith, R. (2002). Maintenance engineering handbook. New York: McGraw-Hill.
• 14. Moubray, J. (1992). Reliability-centered maintenance. New York: Industrial Press Inc.
• 15. Nowlan, F. S., & Heap, H. F. (1978). Reliability centered maintenance. San Francisco: United States Airlines Publications.
• 16. Raju, J. B., Govinda, R. M., & Murthy, C. S. N. (2018). Reliability analysis and failure rate evaluation of load haul dump machines using weibull distribution analysis. Journal of Mathematical Modeling of Engineering Problems, 5(2), 116-122. https://doi.org/10.18280/mmep.050.
• 17. Rakesh, R., Bobin Cherian, J., & George, M. (2013). FMEA analysis for reducing breakdowns of a sub system in the life care product manufacturing industry. International Journal of Engineering Science and Innovative Technology (IJESIT), 2(2), 218-225.
• 18. Rengith, V. R., & Madhavan, Dilip (2018). Fuzzy FMECA (failure mode effect and criticality analysis) of LNG storage facility. Journal of Loss Prevention in the Process Industries, 56(4), 537-547. https://doi.org/10.1016/j.jlp.2018. 01.002.
• 19. Sachdeva, A., Kumar, P., & Kumar, D. (2009). Maintenance criticality analysis using TOPSIS. IEEE International Conference on Production and Industrial Engineering (pp. 199-203). . https://doi.org/10.1109/IEEM.2009.5373388.
• 20. Seyed-Hosseini, S. M., Safaei, N., & Asgharpour, M. J. (2006). Reprioritization of failures in a system failure mode and effects analysis by decision making trial and evaluation laboratory technique. Reliability Engineering & System Safety, 91(8), 872-881. https://doi.org/10.1016/j.ress.2005.09.005.
• 21. Sharma, R. K., Kumar, D., & Kumar, P. (2005). Systematic failure mode effect analysis (FMEA) using fuzzy Linguistic modelling. International Journal of Quality & Reliability Management, 22(9), 986-1004. https://doi.org/10.1108/02656710510625248.
• 22. Stamatis, D. H. (2003). Failure mode and effect analysis: FMEA from theory to execution. Perth, Australia: Quality Press0-87389-598-3.
• 23. Tay, K. M., & Lim, C. P. (2006). Fuzzy FMEA with a guided rules reduction system for prioritization of failures. International Journal of Quality & Reliability Management, 23(8), 1047-1066.
• 24. Teoh, P. C., & Case, K. (2005). An evaluation of failure modes and effects analysis generation method for conceptual design. International Journal of Computer Integrated Manufacturing, 18(4), 279-293.
• 25. Waeyenbergh, G., & Pintelon, L. (2002). A framework for maintenance concept development. International Journal of Production Economics, 77(3), 299-313.
• 26. Wang, L.-X. (2008). Adaptive fuzzy systems and control - design and stability analysis. Prentice Hall0130996319.
• 27. Yang, G. (2007). Life cycle reliability engineering. New Jersey: John Wiley &Sons, Inc978-0-471-71529-0.
• 28. Zadeh, L. A., & Desoer, C. A. (1965). Fuzzy sets. IEEE Information and Control: Vol. 76, (pp. 338-353). New York: McGraw Hill.
• 29. Zafiropoulos, E. P., & Dialynas, E. N. (2005). Reliability prediction and failure mode effects and criticality analysis of electronic devices using fuzzy logic. International Journal of Quality & Reliability Management, 22(2), 183-200.
• 30. Zimmermann, H. (1996). Fuzzy set theory and its applications (3rd ed.). London: Kluwer Academic Publishershttps://doi.org/10.1007/978-94-010-0646-0.