In this paper, an Intuitionistic Fuzzy TOPSIS model which is based on a score function is proposed for detecting the root cause of failure in an Offshore Boat engine, using groups of expert’s opinions. The study which has provided an alternative approach for failure mode identification and analysis in machines, addresses the machine component interaction failures which is a limitation in existing methods. The results from the study show that although early detection of failures in engines is quite difficult to identify due to the dependency of their systems from each other. However, with the Intuitionistic Fuzzy TOPSIS model which is based on an improved score function such faults/failures are easily detected using expert’s based opinions.
1. P. Kettunen, “Troubleshooting Large-Scale New Product Development Embedded Software Projects,” in Product- Focused Software Process Improvement, vol. 4034, Elsevier B.V., 2006, pp. 61-78.
2. N. Zuber and R. Bajri, “Application of artificial neural networks and principal component analysis on vibration signals for automated fault classification of roller element bearings,” Eksploat. i Niezawodn. - Maint. Reliab., vol. 18, no. 2, pp. 299-306, 2016.
3. A. Balin, H. Demirel, and F. Alarcin, “A Hierarchical Structure for Ship Diesel Engine Trouble-Shooting Problem Using Fuzzy Ahp and Fuzzy Vikor Hybrid Methods,” Brodogradnja, vol. 66, no. 1, pp. 54-65, 2015.
4. D. O. Aikhuele and F. M. Turan, “Need for reliability assessment of parent product before redesigning a new product,” Curr. Sci., vol. 112, no. 1, pp. 10-11, 2017.
5. J. A. Keizer, J.-P. Vos, and J. Halman, “Risks in New Product Development,” 2005.
6. R. S. Martínez, “System Theoretic Process Analysis of Electric Power Steering for Automotive Applications,” 2015.
7. R. K. Sharma, D. Kumar, and P. Kumar, “Systematic failure mode effect analysis (FMEA) using fuzzy linguistic modelling,” Int. J. Qual. Reliab. Manag., vol. 22, no. 9, pp. 986-1004, 2005.
8. M. Shaghaghi and K. Rezaie, “Failure Mode and Effects Analysis Using Generalized Mixture Operators,” J. Optim. Ind. Eng., vol. 11, pp. 1-10, 2012.
9. M. Kangavari, S. Salimi, R. Nourian, L. Omidi, and A. Askarian, “An application of failure mode and effect analysis ( FMEA ) to assess risks in petrochemical industry in Iran,” Iran. J. Heal. Saf. Environ., vol. 2, no. 2, pp. 257- 263, 2015.
10. S. Cebi, M. Celik, C. Kahraman, and I. D. Er, “An expert system towards solving ship auxiliary machinery troubleshooting: SHIPAMTSOLVER,” Expert Syst. Appl., vol. 36, no. 3 PART 2, pp. 7219-7227, 2009.
11. H.-C. Liu, L. Liu, N. Liu, and L.-X. Mao, “Risk evaluation in failure mode and effects analysis with extended VIKOR method under fuzzy environment,” Expert Syst. Appl., vol. 39, no. 17, pp. 12926-12934, 2012.
12. H.-C. Liu, L. Liu, Q. Bian, Q. Lin, N. Dong, and P. Xu, “Failure mode and effects analysis using fuzzy evidential reasoning approach and grey theory,” Expert Syst. Appl., vol. 38, no. 4, pp. 4403-4415, 2011.
13. F. Alarcin, A. Balin, and H. Demirel, “Fuzzy AHP and Fuzzy TOPSIS integrated hybrid method for auxiliary systems of ship main engines,” J. Mar. Eng. Technol., vol. 13, no. 1, pp. 3-11, 2014.
14. Y.-H. He, L.-B. Wang, Z.-Z. He, and M. Xie, “A fuzzy TOPSIS and Rough Set based approach for mechanism analysis of product infant failure,” Eng. Appl. Artif. Intell., vol. 47, pp. 1-13, 2015.
15. G. M. Saurav Datta, Chitrasen Samantra, Siba SankarMahapatra, Goutam Mondal, Partha Sarathi Chakraborty, “Selection of internet assessment vendor using TOPSIS method in fuzzy environment,” Int. J. Bus. Perform. Supply Chain Model., vol. 5, no. 1, pp. 1-27, 2013.
16. K. T. Atanassov, “Intuitionistic fuzzy sets,” Fuzzy Sets Syst., vol. 20, no. 1, pp. 87-96, 1986.
17. Z. Xu and H. Liao, “A survey of approaches to decision making with intuitionistic fuzzy preference relations,” Knowledge-Based Syst., vol. 80, pp. 131-142, 2015.
18. D. O. Aikhuele and F. M. Turan, “A modified exponential score function for troubleshooting an improved locally made Offshore Patrol Boat engine,” J. Mar. Eng. Technol., vol. 4177, no. February, 2017.
19. Z. Xu, S. Member, and H. Liao, “Intuitionistic fuzzyanalytic hierarchy process,” IEEE Trans. Fuzzy Syst., vol. 22, no. 4, pp. 749-761, 2014.
20. D. O. Aikhuele and F. B. M. Turan, “An Improved Methodology for Multi-criteria Evaluations in the Shipping Industry,” Brodogradnja/Shipbuilding, vol. 67, no. 3, pp. 59-72, 2016.
21. Z. Bai, “An Interval-Valued Intuitionistic Fuzzy TOPSIS Method Based on an Improved Score Function,” Sci. World J., vol. 2013, pp. 1-9, 2013.
22. D.-F. Li, “Multiattribute decision making method based on generalized OWA operators with intuitionistic fuzzy sets,” Expert Syst. Appl., vol. 37, no. 12, pp. 8673-8678, 2010.
23. J. Ye, “Multicriteria fuzzy decision-making method based on a novel accuracy function under interval-valued intuitionistic fuzzy environment,” Expert Syst. Appl., vol. 36, no. 3, pp. 6899-6902, 2009.
24. T. Wang, H. Lee, and C. Wu, “A Fuzzy TOPSIS Approach with Subjective Weights and Objective Weights,” in 6th WSEAS International Conference on Applied Computer Science, 2007, pp. 1-6.
25. Ü. Şengül, M. Eren, S. Eslamian Shiraz, V. Gezder, and A. B. Şengül, “Fuzzy TOPSIS method for ranking renewable energy supply systems in Turkey,” Renew. Energy, vol. 75, pp. 617-625, 2015.
26. R. Saad, M. Z. Ahmad, M. S. Abu, and M. S. Jusoh, “Hamming distance method with subjective and objective weights for personnel selection.,” ScientificWorldJournal., vol. 2014, p. 865495, 2014.
27. F. H. Lotfi and R. Fallahnejad, “Imprecise shannon’s entropy and multi attribute decision making,” Entropy, vol. 12, no. 1, pp. 53-62, 2010.
28. Hwang C. L. and Yoon K., Multiple Attribute Decision Making Methods and Applications. Berlin: Springer, 1981.
29. B. Bulgurcu, “Application of TOPSIS Technique for Financial Performance Evaluation of Technology Firms in Istanbul Stock Exchange Market,” Procedia - Soc. Behav. Sci., vol. 62, pp. 1033-1040, 2012.
30. O. Jadidi, T. Hong, and F. Firouzi, “TOPSIS and fuzzy multi-objective model integration for supplier selection problem,” J. Achiev. Mater. Manufactuing Eng., vol. 31, no. 2, pp. 762-769, 2008.
31. A. Azizi, D. O. Aikhuele, and F. S. Souleman, “A Fuzzy TOPSIS Model to Rank Automotive Suppliers,” Procedia Manuf., vol. 2, no. February, pp. 159-164, 2015.
32. S. Pakpour, S. V Olishevska, S. O. Prasher, A. S. Milani, and M. R. Chénier, “DNA extraction method selection for agricultural soil using TOPSIS multiple criteria decisionmaking model,” Am. J. Mol. Biol., vol. Published , no. October, pp. 215-228, 2013.
33. M. D. Soufi, B. Ghobadian, G. Najafi, M. R. Sabzimaleki, and T. Yusaf, “TOPSIS multi-criteria decision modeling approach for biolubricant selection for two-stroke petrol engines,” Energies, vol. 8, no. 12, pp. 13960-13970, 2015.
34. C. Yang and Q. Wu, “Decision Model for Product Design Based on Fuzzy TOPSIS Method,” 2008 Int. Symp. Comput. Intell. Des., pp. 342-345, 2008.
35. M. Ghazanfari, S. Rouhani, and M. Jafari, “A fuzzy TOPSIS model to evaluate the Business Intelligence competencies of Port Community Systems,” Polish Marit. Res., vol. 21, no. 2, pp. 86-96, 2014.
36. X. Zhu, F. Wang, C. Liang, J. Li, and X. Sun, “Quality credit evaluation based on TOPSIS: Evidence from airconditioning market in China,” Procedia Comput. Sci., vol. 9, no. 10, pp. 1256-1262, 2012.
37. D. O. Aikhuele and F. B. M. Turan, “Intuitionistic fuzzybased model for failure detection,” Springerplus, vol. 5, no. 1, p. 1938, 2016.
38. F. Lu, J. Huang, and Y. Xing, “Fault diagnostics for turboshaft engine sensors based on a simplified on-board model,” Sensors (Switzerland), vol. 12, no. 8, pp. 11061-11076, 2012.