@InProceedings{BaptistaMeOlVaRiRo:2021:OnClNo,
               author = "Baptista, Gabriel Soares and Mello, Lucas Henrique Sousa and 
                         Oliveira-Santos, Thiago and Varej{\~a}o, Fl{\'a}vio Miguel and 
                         Ribeiro, Marcos Pellegrini and Rodrigues, Alexandre Loureiros",
          affiliation = "{Universidade Federal do Esp{\'{\i}}rito Santo } and 
                         {Universidade Federal do Esp{\'{\i}}rito Santo } and 
                         {Universidade Federal do Esp{\'{\i}}rito Santo } and 
                         {Universidade Federal do Esp{\'{\i}}rito Santo } and 
                         CENPES/Petrobras  and {Universidade Federal do Esp{\'{\i}}rito 
                         Santo}",
                title = "One-Class Classifiers for Novelties Detection in Electrical 
                         Submersible Pumps",
            booktitle = "Proceedings...",
                 year = "2021",
               editor = "Paiva, Afonso and Menotti, David and Baranoski, Gladimir V. G. and 
                         Proen{\c{c}}a, Hugo Pedro and Junior, Antonio Lopes Apolinario 
                         and Papa, Jo{\~a}o Paulo and Pagliosa, Paulo and dos Santos, 
                         Thiago Oliveira and e S{\'a}, Asla Medeiros and da Silveira, 
                         Thiago Lopes Trugillo and Brazil, Emilio Vital and Ponti, Moacir 
                         A. and Fernandes, Leandro A. F. and Avila, Sandra",
         organization = "Conference on Graphics, Patterns and Images, 34. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "eletrical submersible pump, one class classification, anomaly 
                         detection, machine learning.",
             abstract = "Detecting anomalies and fault novelties is of high interest in the 
                         industry due to the scarcity of fault examples to train 
                         classification systems. In this article two algorithms for anomaly 
                         detection, One-Class SVM and Isolation Forest, are successfully 
                         used as effective methods for detecting fault novelties in 
                         problems of electrical submersible pumps. Faults in submersible 
                         electric pumps generate an enormous cost for companies in the oil 
                         and gas sector, since the cost of stopping production to change 
                         the equipment is excessive, which makes it necessary to identify 
                         problems before implementation. Empirical evaluation shows that 
                         both one-class classifiers performed satisfactorily, obtaining 
                         macro f-measure values of approximately 0.86. For comparison 
                         purposes, a Random Forest trained in a conventional binary 
                         classification manner is tested and achieved a macro f-measure of 
                         0.95. Results show that the proposed solutions can have practical 
                         applications in the classification of problems in electrical 
                         submersible pumps, changing the way the oil and gas industry 
                         addresses this difficulty.",
  conference-location = "Gramado, RS, Brazil (virtual)",
      conference-year = "18-22 Oct. 2021",
                  doi = "10.1109/SIBGRAPI54419.2021.00061",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI54419.2021.00061",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/45CUJSF",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/45CUJSF",
           targetfile = "62.pdf",
        urlaccessdate = "2024, May 06"
}