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Recent Advances in Computer Science and Communications

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ISSN (Print): 2666-2558
ISSN (Online): 2666-2566

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Research Article

IFWG-TOPSIS Model for Supporting Infant Failure Assessment in an Offshore Wind Turbine System

Author(s): Daniel O. Aikhuele*, Desmond Eseoghene Ighravwe and Olubayo Moses Babatunde

Volume 15, Issue 4, 2022

Published on: 14 September, 2020

Article ID: e220322185885 Pages: 8

DOI: 10.2174/2666255813999200914112838

Price: $65

Abstract

Introduction: System failure analysis is an essential aspect of equipment management. This analysis improves equipment reliability and availability. However, to assess infant failure under dynamic criteria, reliability engineers require special models.

Methods: Hence, this study uses an Intuitionistic Fuzzy Weighted Geometric (IFWG) and Intuitionistic Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methods to develop an IFWG-TOPSIS model for infant failure assessment. We consider a case study of Offshore Wind (OFW) turbine infant failure assessment.

Results: During the model evaluation, this study considered an infant failure of the turbine's main shaft, blade bearings, pitch system, jacket and monopile support structure, and gearbox. Risk factor, spare part weight, technical importance, cost, and complexity criteria were used to evaluate these components’ reliability. The results show that the blade bearings (S2) and main shaft are the most and least reliable components, respectively. To validate the model’s performance, we compared its results with Gümüş and Bali’s and standard VIKOR models results. These models selected the same components as the most and least reliable components, respectively. Thus, the proposed model is suitable for OFW turbine’s infant failure assessment.

Discussion: It can be deduced that the use of the modified IFWG operator to calculate the intuitionistic fuzzy distance measure in standard TOPSIS model as the capacity to produce realistic results that can compete with existing decision-making methods.

Conclusion: This study has investigated the use of techno-economic criteria for OFW turbine components’ infant failure assessment. A fuzzy-based model was used to establish the connection between the criteria and the components. We developed this model using the Intuitionistic Fuzzy Weighted Geometric (IFWG) and Intuitionistic Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methods. Using experts’ judgments, data were obtained for the developed model evaluation and validation.

Keywords: IFWG-TOPSIS model, offshore wind turbine system, Multi-criteria decision-making, Intuitionistic fuzzy set, infant failure assessment, reliability, dynamic criteria.

Graphical Abstract

[1]
K.E. Knutsen, and G. Manno, "Beyond condition monitoring in the maritime industry", DNV GL Strateg. Res. Innov. Position Pap., vol. 6, pp. 1-31, 2014.
[2]
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. 25-37, 2016.
[3]
D.O. Aikhuele, "A study of product development engineering and design reliability concerns", Int. J. Appl. Ind. Eng., vol. 5, no. 1, pp. 79-89, 2018.
[4]
M. Debruyne, M. Rudy, A. Griffin, S. Hart, E.J. Hultink, and H. Robben, "The impact of new product launch strategies on competitive reaction in industrial markets", J. Prod. Innov. Manage., vol. 19, no. 2, pp. 159-170, 2002.
[5]
H.C. Liu, X.J. Fan, P. Li, and Y.Z. Chen, "Evaluating the risk of failure modes with extended MULTIMOORA method under fuzzy environment", Eng. Appl. Artif. Intell., vol. 34, pp. 168-177, 2014.
[6]
K. Shakesby, Sixty-eight percent of vessel failures can be avoided,” The Maritime Executive, 2016. [Online]. Available at: https://www.maritime-executive.com/blog/sixty-eight-percent-of-vessel-failures-can-be-avoided [Accessed: 17-Dec-2021].
[7]
D.N. Murthy, "Product reliability and warranty: An overview and future research", Production, vol. 17, no. 3, pp. 426-434, 2007.
[8]
D.O. Aikhuele, "Hybrid-fuzzy techniques with flexibility and attitudinal parameters for supporting early product design and reliability management", Univ. Malaysia Pahang, vol. 1, pp. 1-217, 2017.
[9]
W.J. Roesch, "Using a new bathtub curve to correlate quality and reliability", Microelectron. Reliab., vol. 52, no. 12, pp. 2864-2869, 2012.
[10]
J.A. Cruz, Applicability and limitations of reliability allocation methods., NASA Technical Reports Server, 2016, pp. 1-20.
[11]
S.A. Afolalu, I.V. Ihebom, R.R. Elewa, S.U. Ayuba, and K. Oluyemi, "Hybrid method in the reliability allocation in an industry-a review", Int. J. Adv. Res. (Indore), vol. 6, no. 3, pp. 770-773, 2014.
[12]
W. Kuo, and V.R. Prasad, "An annotated overview of system-reliability optimization", IEEE Trans. Reliab., vol. 49, no. 2, pp. 176-187, 2000.
[13]
M.R. Lyu, S. Rangarajan, and A.P. Van Moorsel, "Optimization of reliability allocation and testing schedule for software systems", Proceedings of the Eighth International Symposium on Software Reliability Engineering, 1997pp. 336-347
[14]
K. Pardeep, D.K. Chaturvedi, and G.L. Pahuja, "An efficient heuristic algorithm for determining optimal", Reliab. Theory Appl., vol. 1, no. 18, pp. 15-28, 2010.
[15]
C. Ha, and W. Kuo, "Initial allocation compensation algorithm for redundancy allocation: The scanning heuristic", IIE Trans., vol. 40, no. 7, pp. 678-689, 2008.
[16]
W. Kuo, and R. Wan, "Recent advances in optimal reliability allocation", IEEE Trans. Syst. Man Cybern. A Syst. Hum., vol. 37, no. 2, pp. 143-156, 2007.
[17]
A. Mettas, "Reliability allocation and optimization for complex systems", Proceedings of Annual Reliability and Maintainability Symposium, 2000pp. 216-221
[18]
H. Garg, and S.P. Sharma, "Reliability – redundancy allocation problem of pharmaceutical plant", J. Eng. Sci. Technol., vol. 8, no. 2, pp. 190-198, 2013.
[19]
W. Kuo, V.R. Prasad, F.A. Tillman, and C. Hwang, Optimal reliability design- Fundamentals and Application., Cambridge, UK Cambridge University Press, 2001.
[20]
K.K. Govil, and R.A. Agarwala, "Lagrange multiplier method for optimal reliability allocation in a series system", Reliab. Eng., vol. 6, no. 3, pp. 181-190, 1983.
[21]
Y. Chang, K. Chang, and C. Liaw, "Innovative reliability allocation using the maximal entropy ordered weighted", Comput. Ind. Eng., vol. 57, no. 4, pp. 1274-1281, 2009.
[22]
J. Cheng, F. Zhou, and S. Yang, "“A reliability allocation model and application in designing a mine ventilation system,” IJST", Trans. Civ. Eng., vol. 38, no. C1, pp. 61-73, 2015.
[23]
G. Di Bona, A. Forcina, A. Petrillo, F. De Felice, and A. Silvestri, "A-IFM reliability allocation model based on multicriteria approach A-IFM reliability allocation model based on multicriteria approach", Int. J. Qual. Reliab. Manage., vol. 33, no. 5, pp. 676-698, 2016.
[24]
M. Jureczko, M. Pawlak, and A. Mȩzyk, "Optimisation of wind turbine blades", J. Mater. Process. Technol., vol. 167, no. 2-3, pp. 463-471, 2005.
[25]
J.C.Y. Lee, and J.K. Lundquist, "Evaluation of the wind farm parameterization in the weather research and forecasting model (version 3.8.1) with meteorological and turbine power data", Geosci. Model Dev., vol. 10, no. 11, pp. 4229-4244, 2017.
[26]
D.O. Aikhuele, Intuitionistic fuzzy model for reliability management in wind turbine system., Appl. Comput. Inform, 2018, pp. 1-19.
[27]
E. Hao, and C. Liu, "Evaluation and comparison of anti-impact performance to offshore wind turbine foundations: Monopile, tripod, and jacket", Ocean Eng., vol. 130, pp. 218-227, 2017.
[28]
A. Hassanzadeh, A. Hassanzadeh Hassanabad, and A. Dadvand, "Aerodynamic shape optimization and analysis of small wind turbine blades employing the Viterna approach for post-stall region", Alexandria Eng. J., vol. 55, no. 3, pp. 2035-2043, 2016.
[29]
D. Latinopoulos, and K. Kechagia, "A GIS-based multi-criteria evaluation for wind farm site selection. A regional scale application in Greece", Renew. Energy, vol. 78, pp. 550-560, 2015.
[30]
L. Cradden, C. Kalogeri, I.M. Barrios, G. Galanis, D.M. Ingram, and G. Kallos, "Multi-criteria site selection for offshore renewable energy platforms", Renew. Energy, vol. 87, pp. 791-806, 2016.
[31]
D. Pamucar, L. Gigovic, Z. Bajic, and M. Janoševic, "Location selection for wind farms using GIS multi-criteria hybrid model: An approach based on fuzzy and rough numbers", Sustain., vol. 9, no. 8, pp. 1-23, 2017.
[32]
G. Villacreses, G. Gaona, J. Martínez-Gómez, and D.J. Jijón, "Wind farms suitability location using Geographical Information System (GIS), based on Multi-Criteria Decision Making (MCDM) methods: The case of continental ecuador", Renew. Energy, vol. 109, pp. 275-286, 2017.
[33]
A.H. Lee, M.C. Hung, H.Y. Kang, and W.L. Pearn, "A wind turbine evaluation model under a multi-criteria decision making environment", Energy Convers. Manage., vol. 64, pp. 289-300, 2012.
[34]
S. Pfaffel, S. Faulstich, and K. Rohrig, "Performance and reliability of wind turbines : A review", Energies, p. Vol. 10, No. 1904, pp. 1-27, Nov 2017..
[35]
Z. Jiang, W. Hu, W. Dong, Z. Gao, and Z. Ren, "Structural reliability analysis of wind turbines : A review", Energies, vol. 10, no. 12, p. 2099, 2017.
[36]
E. Lozano-Minguez, A.J. Kolios, and F. Brennan, "Multi-criteria assessment of offshore wind turbine support structures", Renew. Energy, vol. 36, no. 11, pp. 2831-2837, 2011.
[37]
W. Dong, "Torgeir Moan and Z. Gao, “Fatigue reliability analysis of the jacket support structure for offshore wind turbine considering the effect of corrosion and inspection", Reliab. Eng. Syst. Saf., vol. 106, pp. 11-27, 2012.
[38]
K.T. Atanassov, "Intuitionistic fuzzy sets", Fuzzy Sets Syst., vol. 20, no. 1, pp. 87-96, 1986.
[39]
S. Gümüş, and O. Bali, "Dynamic aggregation operators based on intuitionistic fuzzy tools and einstein operations", Fuzzy Inf. Eng., vol. 9, no. 1, pp. 45-65, 2017.
[40]
Z. Xu, and R.R. Yager, "Some geometric aggregation operators based on intuitionistic fuzzy sets", Int. J. Gen. Syst., vol. 35, no. 4, pp. 417-433, 2006.
[41]
H.C. Liu, L. Liu, and P. Li, "Failure mode and effects analysis using intuitionistic fuzzy hybrid weighted Euclidean distance operator", Int. J. Syst. Sci., vol. 45, no. 10, pp. 2012-2030, 2014.
[42]
C.L. Hwang, and K. Yoon, Multiple Attribute Decision Making Methods and Applications., Berlin Springer, 1981.
[43]
D.O. Aikhuele, and D.E. Ighravwe, "Dynamic decision-making method for design concept evaluation based on sustainability criteria In", Sustainability Modeling in Engineering, p. pp. 253-270, 2019.
[44]
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. 17, no. 1, pp. 52-58, 2018.

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