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
Abstract
Keywords
References
Abdelhadi, A. (2018). Maintenance scheduling based on PROMETHEE method in conjunction with group technology philosophy.
Abdelhadi, A., Alwan, L. C., & Yue, X. (2015). Managing storeroom operations using cluster-based preventative maintenance.
Ajukumar, V. N., & Gandhi, O. (2013). Evaluation of green maintenance initiatives in design and development of mechanical systems using an integrated approach.
Alinezhad, A., & Esfandiari, N. (2012). Sensitivity analysis in the QUALIFLEX and VIKOR methods.
Almeida, A. T. (2012). Multicriteria model for selection of preventive maintenance intervals.
Almeida, C. F. M., & Kagan, N. (2010). Allocation of power quality meters by genetic algorithms and fuzzy sets theory.
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.
Amruthnath, N., & Gupta, T. (2018). A research study on unsupervised machine learning algorithms for early fault detection in predictive maintenance. In
Azadeh, A., Asadzadeh, S. M., & Tanhaeean, M. (2017). A consensus-based AHP for improved assessment of resilience engineering in maintenance organizations.
Balin, A., Demirel, H., & Alarcin, F. (2016). An evaluation approach for eliminating the failure effect in gas turbine using fuzzy multiple criteria.
Bertolini, M., & Bevilacqua, M. (2006). A multi attribute utility theory approach to FMECA implementation in the food industry. In
Bertolini, M., Esposito, G., & Romagnoli, G. (2020). A TOPSIS-based approach for the best match between manufacturing technologies and product specifications.
Bevilacqua, M., Braglia, M., & Gabbrielli, R. (2000). Monte Carlo simulation approach for a modified FMECA in a power plant.
Brans, J. P., & De Smet, Y. (2016). PROMETHEE methods.
Brasil, Ministério de Minas e Energia. (2019).
Carnero, M. C. (2014). Multicriteria model for maintenance benchmarking.
Carnero, M. C. (2017). Asymmetries in the maintenance performance of spanish industries before and after the recession.
Carnero, M. C., & Gómez, A. (2017). Maintenance strategy selection in electric power distribution systems.
Chakraborty, S., & Zavadskas, E. K. (2014). Applications of WASPAS method in manufacturing decision making.
Chinnam, R. B., & Baruah, P. (2009). Autonomous diagnostics and prognostics in machining processes through competitive learning-driven HMM-based clustering.
Daher, A., Hoblos, G., Khalil, M., & Chetouani, Y. (2020). New prognosis approach for preventive and predictive maintenance: application to a distillation column.
Dasuki Yusoff, M., Ooi, C. S., Lim, H., & Leong, M. S. (2019). A hybrid k-means-GMM machine learning technique for turbomachinery condition monitoring.
Di Maio, F., Hu, J., Tse, P., Pecht, M., Tsui, K., & Zio, E. (2012). Ensemble-approaches for clustering health status of oil sand pumps.
Diakoulaki, D., Mavrotas, G., & Papayannakis, L. (1995). Determining objective weights in multiple criteria problems: the critic method.
Dong, C., & Bi, K. (2020). A low-carbon evaluation method for manufacturing products based on fuzzy mathematics.
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.
Emovon, I., & Samuel, D. (2017). Prioritising alternative solutions to power generation problems using MCDM techniques: Nigeria as case study.
Emovon, I., Norman, R. A., & Murphy, A. J. (2017). The development of a model for determining scheduled replacement intervals for marine machinery systems.
Frieß, U., Kolouch, M., & Putz, M. (2019).
Frieß, U., Kolouch, M., Friedrich, A., & Zander, A. (2018). Fuzzy-clustering of machine states for condition monitoring.
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).
Ghosh, D., & Roy, S. (2009). A decision-making framework for process plant maintenance.
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.
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.
Kammoun, M. A., & Rezg, N. (2018). Toward the optimal selective maintenance for multi-component systems using observed failure: applied to the FMS study case.
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
Kirubakaran, B., & Ilangkumaran, M. (2016). Selection of optimum maintenance strategy based on FAHP integrated with GRA–TOPSIS.
Kumar, R., & Singal, S. K. (2015). Selection of best operating site of SHP plant based on performance.
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.
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.
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.
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.
Madić, M., & Radovanović, M. (2015). Ranking of some most commonly used nontraditional machining processes using rov and critic methods.
Martin, H., Mohammed, F., Lal, K., & Ramoutar, S. (2019). Maintenance strategy selection for optimum efficiency: application of AHP constant sum.
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
Nikou, T., & Klotz, L. (2014). Application of multi-attribute utility theory for sustainable energy decisions in commercial buildings: a case study.
Opricovic, S., & Tzeng, G. H. (2004). Compromise solution by MCDM methods: a comparative analysis of VIKOR and TOPSIS.
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.
Pal, N. R., Pal, K., Keller, J. M., & Bezdek, J. C. (2005). A possibilistic fuzzy c-means clustering algorithm.
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.
Rastegari, A., & Mobin, M. (2016). Maintenance decision making, supported by computerized maintenance management system. In
Ruschel, E., Santos, E. A. P., & Loures, E. (2017). Industrial maintenance decision-making: a systematic literature review.
Saaty, T. L., & Ergu, D. (2015). When is a decision-making method trustworthy? Criteria for evaluating multi-criteria decision-making methods.
Sadeghpour, H., Tavakoli, A., Kazemi, M., & Pooya, A. (2019). A novel approximate dynamic programming approach for constrained equipment replacement problems: A case study.
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.
Shahmardan, A., & Hendijani Zadeh, M. (2013). An integrated approach for solving a MCDM problem, combination of entropy fuzzy and F-PROMETHEE techniques.
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.
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.
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.
Umamaheswari, E., Ganesan, S., Abirami, M., & Subramanian, S. (2018). Reliability/risk centered cost effective preventive maintenance planning of generating units.
Vafaei, N., Ribeiro, R. A., & Camarinha-Matos, L. M. (2018). Data normalisation techniques in decision making: case study with TOPSIS method.
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.
Wang, Z., Zhang, S., & Kuang, J. (2010). A dynamic MAUT decision model for R&D project selection. In
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
Yanchun, X., Yafei, H., & Hua, H. (2010). Oil analysis and application based on multi-characteristic integration.
Zavadskas, E. K., Antucheviciene, J., Saparauskas, J., & Turskis, Z. (2013). 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.
Zhang, L., Zhang, L., & Shan, H. (2019). Evaluation of equipment maintenance quality: A hybrid multi-criteria decision-making approach.
Submitted date:
06/01/2021
Accepted date:
06/10/2022