Citation:
J. A. Agbakwuru,T. C. Nwaoha,N. E. Udosoh.Application of CRITIC–EDAS-Based Approach in Structural Health Monitoring and Maintenance of Offshore Wind Turbine Systems[J].Journal of Marine Science and Application,2023,(3):545-555.[doi:10.1007/s11804-023-00355-5]
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Application of CRITIC–EDAS-Based Approach in Structural Health Monitoring and Maintenance of Offshore Wind Turbine Systems
J. A. Agbakwuru,T. C. Nwaoha,N. E. Udosoh.Application of CRITIC–EDAS-Based Approach in Structural Health Monitoring and Maintenance of Offshore Wind Turbine Systems[J].Journal of Marine Science and Application,2023,(3):545-555.[doi:10.1007/s11804-023-00355-5]
Info
- Title:
- Application of CRITIC–EDAS-Based Approach in Structural Health Monitoring and Maintenance of Offshore Wind Turbine Systems
- Author(s):
- J. A. Agbakwuru; T. C. Nwaoha; N. E. Udosoh
- Affilations:
- Keywords:
- Offshore wind turbine systems; Maintenance; CRITIC–EDAS; Reliability; Criteria
- DOI:
- 10.1007/s11804-023-00355-5
- Abstract:
- Performing structural health monitoring (SHM) and maintaining an offshore wind turbine system (OWTS) involve periodic observations, analysis, and repairs of the malfunctioning part(s) of the system. In this study, criteria importance through inter criteria correlation–evaluation based on distance from average solution methodology is employed to analyze SHM and maintenance technologies for OWTS. Their various applications are highlighted, and the technologies are prioritized using six indicators, namely, compatibility, potential cost reduction, needed investment, technology maturity, ease of application and potential reliability, and availability and maintainability of the considered technology. The study also aimed to improve the reliability of OWTS and minimize its maintenance cost. The results indicate that the technology’s ease of application, with a weight of 0.201 8, is the most important criterion. Furthermore, mathematical models as an SHM, along with maintenance technology, is ranked as the best alternative with an appraisal score of 0.770 6 and is considered more advantageous than other alternatives. This study provides a new research direction toward improving OWTS reliability. The findings will also aid the decision making of practitioners and researchers in the field of marine and offshore industry in relation to the optimal operations of OWTS.
References:
[1] Animah I, Shafiee M (2019) maintenance strategy selection for critical shipboard machinery systems using a hybrid AHP-PROMETHEE and cost benefit analysis: a case study. Journal of Marine Engineering & Technology 20(3): 1–12. https://doi.org/10.1080/20464177.2019.1572705
[2] Asuquo M, Wang J, Phylip-Jones G, Riahi R (2019) Condition monitoring of marine and offshore machinery using evidential reasoning techniques. Journal of Marine Engineering & Technology 20(1): 1–32. https://doi.org/10.1080/20464177.2019.1573457
[3] Bejger A, Kozak M, Gordon R (2020) the use of acoustic emission elastic waves as diagnosis method for insulated-gate bipolar transistor. Journal of Marine Engineering & Technology 19(2): 186–196. https://www.tandfonline.com/doi/full/10.1080/20464177.2020.1728875
[4] Bhattacharya S (2014) Challenges in design of foundations for offshore wind turbines. Engineering and Technology Reference, 1–9. https://doi.org/10.1049/etr.2014.0041
[5] BladeBUG (2020) Citing the BladeBug information. Available from http://bladebug.co.uk/ [accessed on 25 August 2022]
[6] CEIT Watereye Project (2020) Citing CEIT Watereye project information. Available from https://watereye-project.eu/ [accessed on October 2022]
[7] Chatterjee J, Dethlefs N (2021) Scientometric review of artificial intelligence for operations & maintenance of wind turbines: The past, present and future. Renew. Sustain. Energy Rev. 144: 111051. https://doi.org/10.1016/j.rser.2021.111051
[8] Diakoulaki B, Mavrotas G, Papayannakis L (1995) Determining objective weights in multiple criteria problems: The critic method. Journal of Computer and Operations Research 22(7): 763–770. https://doi.org/10.1016/0305-0548(94)00059-H
[9] Ertel W (2017) Introduction to artificial intelligence. Springer International Publishing, Cham, Germany, 1–21. https://doi.org/10.1007/978-3-319-58487-4_1
[10] Fortuna L, Graziani S, Rizzo A, Xibilia MG (2007) Soft sensors for monitoring and control of industrial processes. Springer, London. https://doi.org/10.1007/978-1-84628-480-9
[11] Ghorabaee MK, Amiri M, Zavadskas EK, Antuchevicience J (2018) A new hybrid fuzzy MCDM approach for evaluation of construction equipment with sustainability considerations. Archives of Civil and Mechanical Engineering 18: 32–49
[12] Ghorabaee KM, Zavadskas EK, Olfat L, Turskis Z (2015) Multi-criteria inventory classification using a new method of evaluation based on distance from average solution (EDAS). Informatica 26(3): 435–451. https://doi.org/10.15388/Informatica.2015.57
[13] Gorcun FO, Kucukonder H (2021) An integrated MCDM approach for evaluating the Ro-Ro marine port selection process: A case study in Black Sea Region. Australian Journal of Maritime & Ocean Affairs 13(3): 203–223. https://doi.org/10.1080/18366503.2021.1878872.
[14] Gorjian N, Ma L, Mittinty M, Yarlagadda P, Sun Y (2010) A review on degradation models in reliability analysis. In: Kiritsis D, Emmanouilidis C, Koronios A, Mathew J (eds.), Engineering Asset Lifecycle Management, Springer, London, 369–384. https://doi.org/10.1007/978-0-85729-320-6_42
[15] Grall A, Dieulle L, Bérenguer C, Roussignol M (2002) Continuous-time predictive-maintenance scheduling for a deteriorating system. IEEE Trans. Reliab. 51(2): 141–150. https://doi.org/10.1109/TR.2002.1011518
[16] Helton K, Tveten M, Stakkeland M, Engebretsen S, Haug O, Aldrin M (2021) Real-time prediction of propulsion motor overheating using machine learning. Journal of Marine Engineering & Technology 21(4): 1–9. https://doi.org/10.1080/20464177.2021.1978745
[17] Iberdrola Renewables (2017). Citing ROMEO Project Information. Available from https://www.romeoproject.eu [Accessed on 16th July 2022]
[18] Jonkman J, Butterfield S, Musial W, Scott G (2009) Definition of a 5-MW reference wind turbine for offshore system development. Technical Report NREL/TP-500-38060, 1–75. https://doi.org/10.2172/947422
[19] Kadlec P, Gabrys B, Strandt S (2009) Data-driven soft sensors in the process industry. Comput. Chem. Eng. 3: 795–814. https://doi.org/10.1016/j.compchemeng.2008.12.012
[20] Kimera D, Nangoo FN (2019) Reliability maintenance aspects of deck machinery for ageing/aged fishing vessels. Journal of Marine Engineering & Technology 21(2): 100–110. https://doi.org/10.1080/20464177.2019.1663595
[21] Kiraci K, Durumuscelebi C (2022) Türkiyé de Havaalani Performansinin CRITIC Temelli EDAS Y?ntemiyle Analizi. Anemon Mus Alparsian üniversitesi Sosyal Bilimler Dergisi. 10(2): 837–856. https://doi.org/10.18506/anemon.964827
[22] Li S, Wang B (2020) Research on evaluating algorithms for the service quality of wireless sensor networks based on interval-valued intuitionistic fuzzy EDAS and CRITIC methods. Mathematical Problems in Engineering 2020: 5391940. https://doi.org/10.1155/2020/5391940
[23] Lu Y, Sun L, Zhang X, Feng F, Kang J, Fu G (2018) Condition based maintenance optimization for offshore wind turbine considering opportunities based on neural network approach. Appl. Ocean Res. 74: 69–79. https://doi.org/10.1016/j.apor.2018.02.016
[24] Madi? M, Radovanovi? M (2015) Ranking of some commonly used non-traditional machining processes using RoV and critic methods. U.P.B. Sci. Bull., Series D, 77(2): 193–204. https://www.scientificbulletin.upb.ro/rev_docs_arhiva/full4e8_598887.pdf
[25] Martinez-Luengo M, Kolios A, Wang L (2016) Structural health monitoring of offshore wind turbines: a review through the statistical pattern recognition paradigm. Renew. Sustain. Energy Rev. 64: 91–105. https://doi.org/10.1016/j.rser.2016.05.085
[26] Nikitas G, Bhattacharya S, Vimalan N (2020) Wind energy. Future Energy, 3rd edition, 331–355. https://doi.org/10.1016/b978-0-08-102886-5.00016-5
[27] ?mer FH, Mutungi H (2016) Assessment of simulation codes for offshore wind turbine foundations. Master thesis, Chalmers University of Technology, Gothenburg, Sweden. https://odr.chalmers.se/items/385baaa9-1456-406d-845f-dfc39f3dc8a6
[28] Papatzimos AK, Thies PR, Dawood T (2019) Offshore wind turbine fault alarm prediction. Wind Energy 22(12): 1779–1788. https://doi.org/10.1002/we.2402
[29] Passon P, Kühn M (2005) State-of-the-art and development needs of simulation codes for offshore wind turbines. Copenhagen Offshore Wind 2005 Conference, 1–12. https://www.researchgate.net/publication/228829674_State-of-the-art_and_development_needs_of_simulation_codes_for_offshore_wind_turbines
[30] Project H (2020) Citing holistic operation and maintenance for energy from offshore wind farms information. Available from http://homeoffshore.org/ [accessed on 16th July 2022]
[31] Rinaldi G, Pillai AC, Thies PR, Johanning L (2019) Multi-objective optimization of the operation and maintenance assets of an offshore wind farm using genetic algorithms. Wind Eng. 44(1): 1–20. https://doi.org/10.1177/0309524X19849826
[32] Science Direct (2020) Citing soft sensor information. Available from https://www.sciencedirect.com/Topics/Materials-Science/Soft-Sensor. [Accessed on 17 July 2022]
[33] Si XS, Wang W, Hu CH, Zhou DH (2011) Remaining useful life estimation-A review on the statistical data driven approaches. Eur. J. Oper. Res. 213(1): 1–14. https://doi.org/10.1016/j.ejor.2010.11.018
[34] Simandjuntak S, Baush N, Farrar A, Ahuir-Torres J, Thomas B, Muna J (2021) iWindCr field trial and electrochemical analysis for corrosion detection and monitoring offshore wind turbine’s MP-TP steel components. Journal of Marine Engineering & Technology 21(6): 311–323. https://doi.org/10.1080/20464177.2021.1949088
[35] Singpurwalla ND (1995) Survival in dynamic environments. Stat. Sci. 10(1): 86–103. https://doi.org/10.1214/ss/1177010132
[36] Soraghan C (2020) Citing Blog Part 1: Machine learning use-cases in the wind industry information. Available from https://ore.catapult.org.uk/blog/part-1-machine-learning-use-cases-in-the-wind-industry/ [accessed on 27 August, 2022]
[37] Tchertchian N, Millet D (2022) Which eco-maintenance for renewable energy? A simulation model for optimising the choice of offshore wind farm maintenance vessel. Journal of Marine Engineering & Technology 22(1): 1–11. https://doi.org/10.1080/20464177.2022.2044584
[38] Tempel JV, Diepeveen N, Vries W, Salzmann CD (2011) Offshore environmental loads and wind turbine design: Impact of wind, wave, currents and ice. Wind Energy Systems, Woodhead Publishing Series in Energy, 463–478. https://doi.org/10.1533/9780857090638.4.463
[39] Van Noortwijk JM (2009) A survey of the application of Gamma processes in maintenance. Reliability Engineering and System Safety 94(1): 2–21. https://doi.org/10.1016/j.ress.2007.03.019
[40] Vanem E, Anreas B (2019) Unsupervised anomaly detection based on clustering methods and sensor data on a marine diesel engine. Journal of Marine Engineering & Technology 20(4): 217–234. https://doi.org/10.1080/20464177.2019.1633223
[41] Wang X, Xu D (2010) An inverse Gaussian process model for degradation data. Technometrics 52(2): 188–197. https://doi.org/10.1198/TECH.2009.08197
[42] Yang K, Hu B, Malekian R, Li Z (2019) An improved control-limit-based principal component analysis method for condition monitoring of marine turbine generators. Journal of Marine Engineering & Technology 19(4): 249–256. https://doi.org/10.1080/20464177.2019.1655135
[43] Yilmaz B, Harmancioglu NB (2010) Multi-criteria decision making for water resource management: A case study of the Gediz River Basin, Turkey. Water S.A 36(5): 563–576. https://doi.org/10.4314/wsa.v36i5.61990
[44] Zavadskas KE, Stevic ?, Turskis Z, Miovan T (2019) A novel extended EDAS in Minkowski space (EDAS-M) method for evaluating autonomous vehicles. Studies in Informatics and Control 28(3): 255–264. https://doi.org/10.24846/v28i3y201902
Memo
- Memo:
-
Received date:2022-12-18;Accepted date:2023-3-3。
Corresponding author:N. E. Udosoh,E-mail:nereusimmanuel@gmail.com
