Diagnosing multiple faults in oil rig motor pumps using support vector machine classifier ensembles
Estefhan Dazzi WandekokemDepartment of Computer Science, Federal University of Espírito Santo, Vitória, ES, BrazilEduardo MendelDepartment of Computer Science, Federal University of Espírito Santo, Vitória, ES, BrazilFábio FabrisDepartment of Computer Science, Federal University of Espírito Santo, Vitória, ES, BrazilMarcelo ValentimDepartment of Computer Science, Federal University of Espírito Santo, Vitória, ES, BrazilRodrigo J. BatistaEspírito Santo Exploration and Production Business Unit Petróleo Brasileiro S.A. PETROBRAS, Vitória, ES, BrazilFlávio Miguel VarejãoDepartment of Computer Science, Federal University of Espírito Santo, Vitória, ES, BrazilThomas W. RauberDepartment of Computer Science, Federal University of Espírito Santo, Vitória, ES, Brazil
2011en
ABI
Annotatsiya
We present a generic procedure for diagnosing faults using features extracted from noninvasive machine signals, based on supervised learning techniques to build the fault classifiers. An important novelty of our research is the use of 2000 examples o
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