Resumen

GARCIA MORA, Félix Antonio; ESCOBAR CHAVEZ, José Luis; GALLEGOS LONDONO, César Marcelo  y  HERNANDEZ DAVILA, Eduardo Segundo. The joint learning approach to gearbox failure detection. Universidad y Sociedad [online]. 2023, vol.15, n.3, pp. 325-333.  Epub 30-Jun-2023. ISSN 2218-3620.

Joint learning is a term used to refer to methods that combine several algorithms to make a decision, usually in supervised machine learning tasks. In recent years logistic regression, Support Vector Machine, neural network and other machine learning algorithms have been used in intelligent fault diagnosis in rotating machinery, however, each algorithm has its advantages and disadvantages and may perform differently for a given data set. In this scientific paper, three gearbox fault detection models are proposed under the ensemble learning approach using the majority voting, soft voting and stacking classification techniques through which the predictions of several estimators are combined in order to improve generalization and robustness over a single estimator. A data set of gearbox failure signals and four classification techniques are used to develop the specified models: logistic regression, support vector machine, XGBoost and random forest. The final accuracy results of the majority voting and soft voting models are 99.86% and 99.82% respectively, while the accuracy of the model under the stacking approach is 99.85%. Compared with the results of the individual classifiers, it is shown that the ensemble learning models compensate for the errors made by the individual classifiers and effectively improve the classification accuracy.

Palabras clave : Joint learning; Majority voting; Soft voting; Stacking; Preventive maintenance.

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