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

被引:0
|
作者
Santiago, Hemir da Cunha [1 ]
Cavalcanti, Jose Carlos da Silva [2 ]
Prudencio, Ricardo Bastos Cavalcante [2 ]
Mohamed, Mohamed A. [3 ]
Sarubbo, Leonie Asfora [4 ]
Converti, Attilio [5 ]
Marinho, Manoel Henrique da Nobrega [1 ]
机构
[1] Univ Pernambuco UPE, Polytech Sch, BR-50720001 Recife, PE, Brazil
[2] Fed Univ Pernambuco UFPE, Informat Ctr, BR-50740560 Recife, PE, Brazil
[3] Minia Univ, Fac Engn, Dept Elect Engn, Al Minya 61519, Egypt
[4] Catholic Univ Pernambuco UNICAP, Dept Biotechnol, BR-50050900 Recife, PE, Brazil
[5] Univ Genoa UNIGE, Dept Civil Chem & Environm Engn, Pole Chem Engn, Via Opera Pia 15, I-16145 Genoa, Italy
关键词
asset maintenance; asset remaining useful life; data analytics; machine learning; models; USEFUL LIFE PREDICTION; ENERGY;
D O I
10.3390/en16062513
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
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.
引用
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页数:24
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