IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i6p2513-d1089686.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/6/2513/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/6/2513/
    Download Restriction: no
    ---><---

    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. Roger Koenker, 2017. "Quantile Regression: 40 Years On," Annual Review of Economics, Annual Reviews, vol. 9(1), pages 155-176, September.
    3. 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.
    4. Ernest H. Forman & Saul I. Gass, 2001. "The Analytic Hierarchy Process---An Exposition," Operations Research, INFORMS, vol. 49(4), pages 469-486, August.
    5. Roger Koenker, 2017. "Quantile regression 40 years on," CeMMAP working papers CWP36/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    6. 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.
    7. 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.
    8. D. J. Johnstone, 2003. "Replacement Cost Asset Valuation and Regulation of Energy Infrastructure Tariffs," Abacus, Accounting Foundation, University of Sydney, vol. 39(1), pages 1-41, February.
    9. Omer, Abdeen Mustafa, 2008. "Energy, environment and sustainable development," Renewable and Sustainable Energy Reviews, Elsevier, vol. 12(9), pages 2265-2300, December.
    10. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ahmed, Walid M.A., 2021. "Stock market reactions to upside and downside volatility of Bitcoin: A quantile analysis," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
    2. Anne M. Lausier & Shaleen Jain, 2018. "Diversity in global patterns of observed precipitation variability and change on river basin scales," Climatic Change, Springer, vol. 149(2), pages 261-275, July.
    3. Guillen, Montserrat & Bermúdez, Lluís & Pitarque, Albert, 2021. "Joint generalized quantile and conditional tail expectation regression for insurance risk analysis," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 1-8.
    4. Chen, Zhao & Cheng, Vivian Xinyi & Liu, Xu, 2024. "Reprint: Hypothesis testing on high dimensional quantile regression," Journal of Econometrics, Elsevier, vol. 239(2).
    5. Aicha Kharazi & Francesco Ravazzolo, 2023. "Regulatory Collateral Requirements and Delinquency Rate in a Two-Agent New Keynesian Model," Working Paper series 23-03, Rimini Centre for Economic Analysis.
    6. Chong-Chuo Chang & Oshamah Lin Lin & Oshamah Yu-Cheng Chang & Oshamah Kun-Zhan Hsu, 2023. "Impact of Financial Liberalization on Firm Risk," Advances in Decision Sciences, Asia University, Taiwan, vol. 27(3), pages 14-45, September.
    7. Rita Pimentel & Morten Risstad & Sjur Westgaard, 2022. "Predicting interest rate distributions using PCA & quantile regression," Digital Finance, Springer, vol. 4(4), pages 291-311, December.
    8. Christian L. E. Franzke & Herminia Torelló i Sentelles, 2020. "Risk of extreme high fatalities due to weather and climate hazards and its connection to large-scale climate variability," Climatic Change, Springer, vol. 162(2), pages 507-525, September.
    9. Damian Clarke & Manuel Llorca Jaña & Daniel Pailañir, 2023. "The use of quantile methods in economic history," Historical Methods: A Journal of Quantitative and Interdisciplinary History, Taylor & Francis Journals, vol. 56(2), pages 115-132, April.
    10. Xolani Sibande & Rangan Gupta & Riza Demirer & Elie Bouri, 2023. "Investor Sentiment and (Anti) Herding in the Currency Market: Evidence from Twitter Feed Data," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 24(1), pages 56-72, January.
    11. Wang, Xuqin & Li, Muyi, 2023. "Bootstrapping the transformed goodness-of-fit test on heavy-tailed GARCH models," Computational Statistics & Data Analysis, Elsevier, vol. 184(C).
    12. Fabio Bellini & Ilaria Peri, 2021. "An axiomatization of $\Lambda$-quantiles," Papers 2109.02360, arXiv.org, revised Jan 2022.
    13. Sulkhan Chavleishvili & Simone Manganelli, 2024. "Forecasting and stress testing with quantile vector autoregression," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(1), pages 66-85, January.
    14. Chen, Le-Yu & Lee, Sokbae, 2023. "Sparse quantile regression," Journal of Econometrics, Elsevier, vol. 235(2), pages 2195-2217.
    15. Rui Evangelista & João Andrade E Silva & Esmeralda A. Ramalho, 2021. "How heterogeneous is the impact of energy efficiency on dwelling prices? Evidence from the application of the unconditional quantile hedonic model to the Portuguese residential market," Working Papers REM 2021/0186, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
    16. Riku-Pekka Nikula & Konsta Karioja & Kauko Leiviskä & Esko Juuso, 2019. "Prediction of mechanical stress in roller leveler based on vibration measurements and steel strip properties," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1563-1579, April.
    17. Chen, Xiaohong & Pouzo, Demian & Powell, James L., 2019. "Penalized sieve GEL for weighted average derivatives of nonparametric quantile IV regressions," Journal of Econometrics, Elsevier, vol. 213(1), pages 30-53.
    18. Francesca Caselli & Mr. Philippe Wingender, 2018. "Bunching at 3 Percent: The Maastricht Fiscal Criterion and Government Deficits," IMF Working Papers 2018/182, International Monetary Fund.
    19. Jayeeta Bhattacharya, 2020. "Quantile regression with generated dependent variable and covariates," Papers 2012.13614, arXiv.org.
    20. Mignon, Valérie & Saadaoui, Jamel, 2025. "Asymmetries in the oil market: accounting for the growing role of China through quantile regressions," Macroeconomic Dynamics, Cambridge University Press, vol. 29, pages 1-1, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:6:p:2513-:d:1089686. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.