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

Hemir da Cunha Santiago, José Carlos da Silva Cavalcanti, Ricardo Bastos Cavalcante Prudêncio, Mohamed A. Mohamed, Leonie Asfora Sarubbo, Attilio Converti () and Manoel Henrique da Nóbrega Marinho
Additional contact information
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

Energies, 2023, vol. 16, issue 6, 1-24

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.

Keywords: asset maintenance; asset remaining useful life; data analytics; machine learning; models (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/16/6/2513/pdf (application/pdf)
https://www.mdpi.com/1996-1073/16/6/2513/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:16:y:2023:i:6:p:2513-:d:1089686

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().