Production
https://prod.org.br/article/doi/10.1590/0103-6513.20210066
Production
Thematic Section - Future of Energy-efficient Operations and Production Systems

Entropy-MAUT integrated approach supported by Fuzzy K-means: a robust tool for determining critical components for maintenance monitoring and a case study of Kaplan hydro generator unit

Marjorie Maria Bellinello; Sara Antomarioni; Gilberto Francisco Martha de Souza; Maurizio Bevilacqua; Fillipo Emanuele Ciarapica

Downloads: 0
Views: 577

Abstract

Paper aims: This paper aims to develop a proper maintenance policy directly related to defining critical components for ensuring a high level of safety and high-level in-service quality for all hydro generator units.

Originality: An innovative integrated tool that contributes to ensuing assertiveness in decision-making to determine the critical components is presented in this study. Specifically, hydro-generator unit type Kaplan belonging to a Brazilian Hydroelectric power plant is used as an application case to highlight the choice of the most suitable maintenance policy in light of the proposed approach. The selection of the case study is based on the fact that hydroelectric power plants are the basis of the Brazilian energy matrix, accounting for 75% of the demand in the country. Therefore, the need to maintain hydroelectric plants' availability and operational reliability is clear not to compromise the continuity and conformity (quality) of the electrical energy supply.

Research method: Seven multi-criteria decision-making methods were applied in addition to two methods for deciding weight (Critic Method and Entropy) have been compared to determine the critical components of the hydro-generator. To investigate the robustness of the classification of the applied Multi-Criteria Decision Making approaches, a sensitivity analysis was performed based on the weight change of each decision criterion.

Main findings: As a main result, the Entropy- Multi-Attribute Utility Theory model is proposed as the best approach to guarantee the selection of critical components for the Brazilian hydroelectric power plant case study. The validation sensitivity analysis by critical Fuzzy K-means groups guarantees that it is a robust tool for decision-making.

Implications for theory and practice: Ensuring the availability and reliability of hydroelectric plants can be achieved by employing appropriate maintenance policies that reduce the likelihood of failure or even eliminate its root causes, preventing failure from occurring. Consequently, a robust tool for decision-making regarding the Kaplan hydro generator's critical components' monitoring was developed.

Keywords

Maintenance policy, Entropy-MAUT, Fuzzy K-means, Energy supply, Decision making

References

Abdelhadi, A. (2018). Maintenance scheduling based on PROMETHEE method in conjunction with group technology philosophy. International Journal of Quality & Reliability Management, 35(7), 1423-1444. http://dx.doi.org/10.1108/IJQRM-03-2017-0053.

Abdelhadi, A., Alwan, L. C., & Yue, X. (2015). Managing storeroom operations using cluster-based preventative maintenance. Journal of Quality in Maintenance Engineering, 21(2), 154-170. http://dx.doi.org/10.1108/JQME-10-2013-0066.

Ajukumar, V. N., & Gandhi, O. (2013). Evaluation of green maintenance initiatives in design and development of mechanical systems using an integrated approach. Journal of Cleaner Production, 51(15), 34-46. http://dx.doi.org/10.1016/j.jclepro.2013.01.010.

Alinezhad, A., & Esfandiari, N. (2012). Sensitivity analysis in the QUALIFLEX and VIKOR methods. Journal of Optimization in Industrial Engineering, 10, 29-34.

Almeida, A. T. (2012). Multicriteria model for selection of preventive maintenance intervals. Quality and Reliability Engineering International, 28(6), 585-593. http://dx.doi.org/10.1002/qre.1415.

Almeida, C. F. M., & Kagan, N. (2010). Allocation of power quality meters by genetic algorithms and fuzzy sets theory. Controle & Automação, 21(4), 363-378. http://dx.doi.org/10.1590/S0103-17592010000400004.

Almomani, M. A., Aladeemy, M., Abdelhadi, A., & Mumani, A. (2013). A proposed approach for setup time reduction through integrating conventional SMED method with multiple criteria decision-making techniques. Computers & Industrial Engineering, 66(2), 461-469. http://dx.doi.org/10.1016/j.cie.2013.07.011.

Amruthnath, N., & Gupta, T. (2018). A research study on unsupervised machine learning algorithms for early fault detection in predictive maintenance. In 2018 5th International Conference on Industrial Engineering and Applications (ICIEA 2018) (pp. 355-361). New York: IEEE. https://doi.org/10.1109/IEA.2018.8387124.

Azadeh, A., Asadzadeh, S. M., & Tanhaeean, M. (2017). A consensus-based AHP for improved assessment of resilience engineering in maintenance organizations. Journal of Loss Prevention in the Process Industries, 47, 151-160. http://dx.doi.org/10.1016/j.jlp.2017.02.028.

Balin, A., Demirel, H., & Alarcin, F. (2016). An evaluation approach for eliminating the failure effect in gas turbine using fuzzy multiple criteria. Transactions of the Royal Institution of Naval Architects Part A: International Journal of Maritime Engineering, 158, 219-230. http://dx.doi.org/10.3940/rina.ijme.a3.377.

Bertolini, M., & Bevilacqua, M. (2006). A multi attribute utility theory approach to FMECA implementation in the food industry. In European Safety and Reliability Conference 2006, ESREL 2006-Safety and Reliability for Managing Risk (pp. 1917-1923). London: Taylor & Francis. Retrieved in 22 June 2022, from https://iris.unimore.it/handle/11380/1188686

Bertolini, M., Esposito, G., & Romagnoli, G. (2020). A TOPSIS-based approach for the best match between manufacturing technologies and product specifications. Expert Systems with Applications, 159, 113610. http://dx.doi.org/10.1016/j.eswa.2020.113610.

Bevilacqua, M., Braglia, M., & Gabbrielli, R. (2000). Monte Carlo simulation approach for a modified FMECA in a power plant. Quality and Reliability Engineering International, 16(4), 313-324. http://dx.doi.org/10.1002/1099-1638(200007/08)16:4<313::AID-QRE434>3.0.CO;2-U.

Brans, J. P., & De Smet, Y. (2016). PROMETHEE methods. International Series in Operations Research and Management Science, 233, 187-219. http://dx.doi.org/10.1007/978-1-4939-3094-4_6.

Brasil, Ministério de Minas e Energia. (2019). Composição da matriz energética brasileira. Retrieved in 22 June 2022, from http://www.mme.gov.br/

Carnero, M. C. (2014). Multicriteria model for maintenance benchmarking. Journal of Manufacturing Systems, 33(2), 303-321. http://dx.doi.org/10.1016/j.jmsy.2013.12.006.

Carnero, M. C. (2017). Asymmetries in the maintenance performance of spanish industries before and after the recession. Symmetry, 9(8), 166. http://dx.doi.org/10.3390/sym9080166.

Carnero, M. C., & Gómez, A. (2017). Maintenance strategy selection in electric power distribution systems. Energy, 129, 255-272. http://dx.doi.org/10.1016/j.energy.2017.04.100.

Chakraborty, S., & Zavadskas, E. K. (2014). Applications of WASPAS method in manufacturing decision making. Informatica, 25(1), 1-20. http://dx.doi.org/10.15388/Informatica.2014.01.

Chinnam, R. B., & Baruah, P. (2009). Autonomous diagnostics and prognostics in machining processes through competitive learning-driven HMM-based clustering. International Journal of Production Research, 47(23), 6739-6758. http://dx.doi.org/10.1080/00207540802232930.

Daher, A., Hoblos, G., Khalil, M., & Chetouani, Y. (2020). New prognosis approach for preventive and predictive maintenance: application to a distillation column. Chemical Engineering Research & Design, 153, 162-174. http://dx.doi.org/10.1016/j.cherd.2019.10.029.

Dasuki Yusoff, M., Ooi, C. S., Lim, H., & Leong, M. S. (2019). A hybrid k-means-GMM machine learning technique for turbomachinery condition monitoring. MATEC Web of Conferences, 255(1), 06008. http://dx.doi.org/10.1051/matecconf/201925506008.

Di Maio, F., Hu, J., Tse, P., Pecht, M., Tsui, K., & Zio, E. (2012). Ensemble-approaches for clustering health status of oil sand pumps. Expert Systems with Applications, 39(5), 4847-4859. http://dx.doi.org/10.1016/j.eswa.2011.10.008.

Diakoulaki, D., Mavrotas, G., & Papayannakis, L. (1995). Determining objective weights in multiple criteria problems: the critic method. Computers & Operations Research, 22(7), 763-770. http://dx.doi.org/10.1016/0305-0548(94)00059-H.

Dong, C., & Bi, K. (2020). A low-carbon evaluation method for manufacturing products based on fuzzy mathematics. Systems Science & Control Engineering, 8(1), 153-161. http://dx.doi.org/10.1080/21642583.2020.1734987.

Drakaki, M., Karnavas, Y. L., Karlis, A. D., Chasiotis, I. D., & Tzionas, P. (2020). Study on fault diagnosis of broken rotor bars in squirrel cage induction motors: a multiagent system approach using intelligent classifiers. IET Electric Power Applications, 14(2), 245-255. http://dx.doi.org/10.1049/iet-epa.2019.0619.

Emovon, I., & Samuel, D. (2017). Prioritising alternative solutions to power generation problems using MCDM techniques: Nigeria as case study. International Journal of Integrated Engineering, 9(3). Retrieved in 22 June 2022, from https://publisher.uthm.edu.my/ojs/index.php/ijie/article/view/1185

Emovon, I., Norman, R. A., & Murphy, A. J. (2017). The development of a model for determining scheduled replacement intervals for marine machinery systems. Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment, 231(3), 723-739. http://dx.doi.org/10.1177/1475090216681345.

Frieß, U., Kolouch, M., & Putz, M. (2019). Deduction of time-dependent machine tool characteristics by fuzzy-clustering (pp. 7-17). Heidelberg: Springer. https://doi.org/10.1007/978-3-662-58485-9_2.

Frieß, U., Kolouch, M., Friedrich, A., & Zander, A. (2018). Fuzzy-clustering of machine states for condition monitoring. CIRP Journal of Manufacturing Science and Technology, 23, 64-77. http://dx.doi.org/10.1016/j.cirpj.2018.09.001.

Ghorabaee, M. K., Zavadskas, E. K., 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. http://dx.doi.org/10.15388/Informatica.2015.57.

Ghosh, D., & Roy, S. (2009). A decision-making framework for process plant maintenance. European Journal of Industrial Engineering, 4(1), 78-98. http://dx.doi.org/10.1504/EJIE.2010.029571.

Goh, C. S., Gupta, M., Jarfors, A. E. W., Tan, M. J., & Wei, J. (2012). Study of camshaft grinders faults prediction based on RBF neural network. Applied Mathematics and Materials, 141, 519-523. http://dx.doi.org/10.4028/www.scientific.net.

Gugaliya, A., Boral, S., & Naikan, V. N. A. (2019). A hybrid decision making framework for modified failure mode effects and criticality analysis: a case study on process plant induction motors. International Journal of Quality & Reliability Management, 36(8), 1266-1283. http://dx.doi.org/10.1108/IJQRM-08-2018-0213.

Kammoun, M. A., & Rezg, N. (2018). Toward the optimal selective maintenance for multi-component systems using observed failure: applied to the FMS study case. International Journal of Advanced Manufacturing Technology, 96(1-4), 1093-1107. http://dx.doi.org/10.1007/s00170-018-1623-8.

Kim, H. G., Yoon, H. S., Yoo, J. H., & Yoon, H. I., & Han, S. S. (2019). Development of predictive maintenance technology for wafer transfer robot using clustering algorithm. In ICEIC 2019 - International Conference on Electronics, Information, and Communication. New York: IEEE. https://doi.org/10.23919/ELINFOCOM.2019.8706485.

Kirubakaran, B., & Ilangkumaran, M. (2016). Selection of optimum maintenance strategy based on FAHP integrated with GRA–TOPSIS. Annals of Operations Research, 245(1-2), 285-313. http://dx.doi.org/10.1007/s10479-014-1775-3.

Kumar, R., & Singal, S. K. (2015). Selection of best operating site of SHP plant based on performance. Procedia: Social and Behavioral Sciences, 189, 110-116. http://dx.doi.org/10.1016/j.sbspro.2015.03.205.

Langone, R., Alzate, C., De Ketelaere, B., Vlasselaer, J., Meert, W., & Suykens, J. A. K. (2015). LS-SVM based spectral clustering and regression for predicting maintenance of industrial machines. Engineering Applications of Artificial Intelligence, 37, 268-278. http://dx.doi.org/10.1016/j.engappai.2014.09.008.

Lima, E., Gorski, E., Loures, E. F. R., Santos, E. A. P., & Deschamps, F. (2019). Applying machine learning to AHP multicriteria decision making method to assets prioritization in the context of industrial maintenance 4.0. IFAC-PapersOnLine, 52(13), 2152-2157. http://dx.doi.org/10.1016/j.ifacol.2019.11.524.

Liu, C., Wang, X., Huang, Y., Liu, Y., Li, R., Li, Y., & Liu, J. (2020). A moving shape-based robust fuzzy K-modes clustering algorithm for electricity profiles. Electric Power Systems Research, 187, 106425. http://dx.doi.org/10.1016/j.epsr.2020.106425.

Lo, H. W., Liou, J. J. H., Huang, C. N., & Chuang, Y. C. (2019). A novel failure mode and effect analysis model for machine tool risk analysis. Reliability Engineering & System Safety, 183, 173-183. http://dx.doi.org/10.1016/j.ress.2018.11.018.

Madić, M., & Radovanović, M. (2015). Ranking of some most commonly used nontraditional machining processes using rov and critic methods. UPB Scientific Bulletin, Series D: Mechanical Engineering, 77(2), 193-2045.

Martin, H., Mohammed, F., Lal, K., & Ramoutar, S. (2019). Maintenance strategy selection for optimum efficiency: application of AHP constant sum. Facilities, 38(5-6), 421-444. http://dx.doi.org/10.1108/F-05-2018-0060.

Mousavi, S. S., Nezami, F. G., Heydar, M., & Aryanejad, M. B. (2009). A hybrid fuzzy group decision making and factor analysis for selectingmaintenance strategy. In 2009 International Conference on Computers and Industrial Engineering, CIE 2009 (pp. 1204-1209). New York: IEEE. http://dx.doi.org/10.1109/ICCIE.2009.5223824.

Nikou, T., & Klotz, L. (2014). Application of multi-attribute utility theory for sustainable energy decisions in commercial buildings: a case study. Smart and Sustainable Built Environment, 3(3), 207-222. http://dx.doi.org/10.1108/SASBE-01-2014-0004.

Opricovic, S., & Tzeng, G. H. (2004). Compromise solution by MCDM methods: a comparative analysis of VIKOR and TOPSIS. European Journal of Operational Research, 156(2), 445-455. http://dx.doi.org/10.1016/S0377-2217(03)00020-1.

Özcan, E. C., Ünlüsoy, S., & Eren, T. (2017). A combined goal programming: AHP approach supported with TOPSIS for maintenance strategy selection in hydroelectric power plants. Renewable & Sustainable Energy Reviews, 78, 1410-1423. http://dx.doi.org/10.1016/j.rser.2017.04.039.

Pal, N. R., Pal, K., Keller, J. M., & Bezdek, J. C. (2005). A possibilistic fuzzy c-means clustering algorithm. IEEE Transactions on Fuzzy Systems, 13(4), 517-530. http://dx.doi.org/10.1109/TFUZZ.2004.840099.

Pérez-Domínguez, L., Sánchez Mojica, K. Y., Ovalles Pabón, L. C., & Cordero Díaz, M. C. (2018). Application of the MOORA method for the evaluation of the industrial maintenance system. Journal of Physics: Conference Series, 1126, 12018. http://dx.doi.org/10.1088/1742-6596/1126/1/012018.

Rastegari, A., & Mobin, M. (2016). Maintenance decision making, supported by computerized maintenance management system. In Proceedings of the Annual Reliability and Maintainability Symposium. New York: IEEE. http://dx.doi.org/10.1109/RAMS.2016.7448086.

Ruschel, E., Santos, E. A. P., & Loures, E. (2017). Industrial maintenance decision-making: a systematic literature review. Journal of Manufacturing Systems, 45, 180-194. http://dx.doi.org/10.1016/j.jmsy.2017.09.003.

Saaty, T. L., & Ergu, D. (2015). When is a decision-making method trustworthy? Criteria for evaluating multi-criteria decision-making methods. International Journal of Information Technology & Decision Making, 14(6), 1171-1187. http://dx.doi.org/10.1142/S021962201550025X.

Sadeghpour, H., Tavakoli, A., Kazemi, M., & Pooya, A. (2019). A novel approximate dynamic programming approach for constrained equipment replacement problems: A case study. Advances in Production Engineering & Management, 14(3), 355-366. http://dx.doi.org/10.14743/apem2019.3.333.

Salehi, V., Zarei, H., Shirali, G. A., & Hajizadeh, K. (2020). An entropy-based TOPSIS approach for analyzing and assessing crisis management systems in petrochemical industries. Journal of Loss Prevention in the Process Industries, 67, 104241. http://dx.doi.org/10.1016/j.jlp.2020.104241.

Shahmardan, A., & Hendijani Zadeh, M. (2013). An integrated approach for solving a MCDM problem, combination of entropy fuzzy and F-PROMETHEE techniques. Journal of Industrial Engineering and Management, 6(4), 1124-1138. http://dx.doi.org/10.3926/jiem.899.

Siksnelyte-Butkiene, I., Zavadskas, E. K., & Streimikiene, D. (2020). Multi-Criteria Decision-Making (MCDM) for the assessment of renewable energy technologies in a household: a review. Energies, 13(5), 1164. http://dx.doi.org/10.3390/en13051164.

Soltanali, H., Garmabaki, A. H. S., Thaduri, A., Parida, A., Kumar, U., & Rohani, A. (2019). Sustainable production process: an application of reliability, availability, and maintainability methodologies in automotive manufacturing. J Risk and Reliability, 233(4), 682-697. http://dx.doi.org/10.1177/1748006X18818266.

Stefano, N. M., Casarotto Filho, N., Garcia Lupi Vergara, L., & Garbin Da Rocha, R. U. (2015). COPRAS (Complex Proportional Assessment): state of the art research and its applications. IEEE Latin America Transactions, 13(12), 3899-3906. http://dx.doi.org/10.1109/TLA.2015.7404925.

Umamaheswari, E., Ganesan, S., Abirami, M., & Subramanian, S. (2018). Reliability/risk centered cost effective preventive maintenance planning of generating units. International Journal of Quality & Reliability Management, 35(9), 2052-2079. http://dx.doi.org/10.1108/IJQRM-03-2017-0039.

Vafaei, N., Ribeiro, R. A., & Camarinha-Matos, L. M. (2018). Data normalisation techniques in decision making: case study with TOPSIS method. International Journal of Information and Decision Sciences, 10(1), 19-38. http://dx.doi.org/10.1504/IJIDS.2018.090667.

Wang, H., Chen, J., Qu, J., & Ni, G. (2020). A new approach for safety life prediction of industrial rolling bearing based on state recognition and similarity analysis. Safety Science, 122, 104530. http://dx.doi.org/10.1016/j.ssci.2019.104530.

Wang, Z., Zhang, S., & Kuang, J. (2010). A dynamic MAUT decision model for R&D project selection. In Proceedings of the 2010 International Conference on Computing, Control and Industrial Engineering (CCIE 2010) (pp. 423-427). New York: IJCAI. http://dx.doi.org/10.1109/CCIE.2010.112.

Xu, J., Han, J., Xiong, K., & Nie, F. (2016). Robust and sparse fuzzy K-means clustering video understanding view project hyperspectral images clustering view project robust and sparse fuzzy K-means clustering. In Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI-16) (pp. 2224-2230). IJCAI. Retrieved in 22 June 2022, from https://www.researchgate.net/publication/314152643

Yanchun, X., Yafei, H., & Hua, H. (2010). Oil analysis and application based on multi-characteristic integration. Industrial Lubrication and Tribology, 62(5), 298-303. http://dx.doi.org/10.1108/00368791011064464.

Zavadskas, E. K., Antucheviciene, J., Saparauskas, J., & Turskis, Z. (2013). MCDM methods WASPAS and MULTIMOORA: verification of robustness of methods when assessing alternative solutions. Economic Computation and Economic Cybernetics Studies and Research, 47(2), 5-20. Retrieved in 22 June 2022, from https://www.researchgate.net/publication/287762606_MCDM_methods_WASPAS_and_MULTIMOORA_Verification_of_robustness_of_methods_when_assessing_alternative_solutions

Zavadskas, E. K., Kaklauskas, A., Turskis, Z., & Tamošaitienė, J. (2008). Selection of the effective dwelling house walls by applying attributes values determined at intervals. Journal of Civil Engineering and Management, 14(2), 85-93. http://dx.doi.org/10.3846/1392-3730.2008.14.3.

Zhang, L., Zhang, L., & Shan, H. (2019). Evaluation of equipment maintenance quality: A hybrid multi-criteria decision-making approach. Advances in Mechanical Engineering, 11(3), 168781401983601. http://dx.doi.org/10.1177/1687814019836013.
 


Submitted date:
06/01/2021

Accepted date:
06/10/2022

62cc7657a953952a636e6983 production Articles
Links & Downloads

Production

Share this page
Page Sections