Using a Support Vector Machine Based Decision Stage to Improve the Fault Diagnosis on Gearboxes

Rodrigo P. Monteiro, Mariela Cerrada, DIego R. Cabrera, René V. Sánchez, Carmelo J.A. Bastos-Filho

Resultado de la investigación: Contribución a una revistaArtículorevisión exhaustiva

16 Citas (Scopus)

Resumen

Gearboxes are mechanical devices that play an essential role in several applications, e.g., the transmission of automotive vehicles. Their malfunctioning may result in economic losses and accidents, among others. The rise of powerful graphical processing units spreads the use of deep learning-based solutions to many problems, which includes the fault diagnosis on gearboxes. Those solutions usually require a significant amount of data, high computational power, and a long training process. The training of deep learning-based systems may not be feasible when GPUs are not available. This paper proposes a solution to reduce the training time of deep learning-based fault diagnosis systems without compromising their accuracy. The solution is based on the use of a decision stage to interpret all the probability outputs of a classifier whose output layer has the softmax activation function. Two classification algorithms were applied to perform the decision. We have reduced the training time by almost 80% without compromising the average accuracy of the fault diagnosis system.

Idioma originalInglés
Número de artículo1383752
Páginas (desde-hasta)1383752
PublicaciónComputational Intelligence and Neuroscience
Volumen2019
DOI
EstadoPublicada - 1 ene. 2019

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