Álvarez Palomeque, Lourdes XimenaJadán Avilés, Diana CarolinaCerrada Lozada, MarielaSánchez, René VinicioMacancela Poveda, Jean Carlo2018-10-252018-10-252018978-153863894-10000-0000http://dspace.ucuenca.edu.ec/handle/123456789/31478https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85045739957&origin=inwardThis paper describes a method for fault diagnosis in gearboxes using features extracted from the Poincare plot of the vibration signal. Several features describing the geometrical shape of the Poincare plot are calculated and three of these features are selected for performing the classification of 10 types of faults recorded in the gearbox vibration signal dataset. A multi-class Error-Correcting Output Code Support Vector Machine is trained for performing the classification of faults. The cross-validation performed show that the highest accuracy attained is 95.3% when signals recorded using the load L1 are considered.This paper describes a method for fault diagnosis in gearboxes using features extracted from the Poincare plot of the vibration signal. Several features describing the geometrical shape of the Poincare plot are calculated and three of these features are selected for performing the classification of 10 types of faults recorded in the gearbox vibration signal dataset. A multi-class Error-Correcting Output Code Support Vector Machine is trained for performing the classification of faults. The cross-validation performed show that the highest accuracy attained is 95.3% when signals recorded using the load L1 are considered.es-ESGearbox Faults ClassificationMulti-Class Support Vector MachinesPoincare PlotsRotatory MachinesARTÍCULO DE CONFERENCIA10.1109/ETCM.2017.8247500