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A Novel Remaining Useful Estimation Model to Assist Asset Renewal Decisions Applied to the Brazilian Electric Sector

Author

Listed:
  • Hemir da Cunha Santiago

    (Polytechnic School, University of Pernambuco (UPE), Recife 50720-001, PE, Brazil)

  • José Carlos da Silva Cavalcanti

    (Informatics Center, Federal University of Pernambuco (UFPE), Recife 50740-560, PE, Brazil)

  • Ricardo Bastos Cavalcante Prudêncio

    (Informatics Center, Federal University of Pernambuco (UFPE), Recife 50740-560, PE, Brazil)

  • Mohamed A. Mohamed

    (Department of Electrical Engineering, Faculty of Engineering, Minia University, Minia 61519, Egypt)

  • Leonie Asfora Sarubbo

    (Department of Biotechnology, Catholic University of Pernambuco (UNICAP), Recife 50050-900, PE, Brazil)

  • Attilio Converti

    (Department of Civil, Chemical and Environmental Engineering, University of Genoa (UNIGE), Pole of Chemical Engineering, Via Opera Pia 15, 16145 Genoa, Italy)

  • Manoel Henrique da Nóbrega Marinho

    (Polytechnic School, University of Pernambuco (UPE), Recife 50720-001, PE, Brazil)

Abstract

Assets deteriorate over time, as well as being covered, corroded, or becoming old in less obvious ways. Maintenance can extend the remaining useful life (RUL) of an asset system, but sooner or later it must surely be replaced. In this study, we propose a new RUL estimation methodology to assist in decision making for the maintenance and replacement of assets from prioritizing equipment in a renovation plan. Our methodology uses advanced data analysis techniques that consider multiple competing criteria with the goal of maximizing values of the asset throughout its life cycle, while considering the rules of remuneration and service quality of the current regulation, as well as the values at risk according to the decisions and actions taken. Experimental results with real datasets show the efficiency of the proposed approach. Finally, this work also presents the development of an analytical tool to optimize asset renewal decisions applying the RUL estimation methodology proposed and its application to the Brazilian electric sector.

Suggested Citation

  • Hemir da Cunha Santiago & José Carlos da Silva Cavalcanti & Ricardo Bastos Cavalcante Prudêncio & Mohamed A. Mohamed & Leonie Asfora Sarubbo & Attilio Converti & Manoel Henrique da Nóbrega Marinho, 2023. "A Novel Remaining Useful Estimation Model to Assist Asset Renewal Decisions Applied to the Brazilian Electric Sector," Energies, MDPI, vol. 16(6), pages 1-24, March.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:6:p:2513-:d:1089686
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    References listed on IDEAS

    as
    1. Tso, Geoffrey K.F. & Yau, Kelvin K.W., 2007. "Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks," Energy, Elsevier, vol. 32(9), pages 1761-1768.
    2. A. Mosallam & K. Medjaher & N. Zerhouni, 2016. "Data-driven prognostic method based on Bayesian approaches for direct remaining useful life prediction," Journal of Intelligent Manufacturing, Springer, vol. 27(5), pages 1037-1048, October.
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    4. Si, Xiao-Sheng & Wang, Wenbin & Hu, Chang-Hua & Zhou, Dong-Hua, 2011. "Remaining useful life estimation - A review on the statistical data driven approaches," European Journal of Operational Research, Elsevier, vol. 213(1), pages 1-14, August.
    5. Omer, Abdeen Mustafa, 2008. "Energy, environment and sustainable development," Renewable and Sustainable Energy Reviews, Elsevier, vol. 12(9), pages 2265-2300, December.
    6. Ahmed Ragab & Mohamed-Salah Ouali & Soumaya Yacout & Hany Osman, 2016. "Remaining useful life prediction using prognostic methodology based on logical analysis of data and Kaplan–Meier estimation," Journal of Intelligent Manufacturing, Springer, vol. 27(5), pages 943-958, October.
    7. Diogo M. F. Izidio & Paulo S. G. de Mattos Neto & Luciano Barbosa & João F. L. de Oliveira & Manoel Henrique da Nóbrega Marinho & Guilherme Ferretti Rissi, 2021. "Evolutionary Hybrid System for Energy Consumption Forecasting for Smart Meters," Energies, MDPI, vol. 14(7), pages 1-19, March.
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