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Exploiting generative adversarial networks as an oversampling method for fault diagnosis of an industrial robotic manipulator
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Orientador(es)
Resumo(s)
Data-driven machine learning techniques play an important role in fault diagnosis, safety,
and maintenance of the industrial robotic manipulator. However, these methods require data that,
more often that not, are hard to obtain, especially data collected from fault condition states and,
without enough and appropriated (balanced) data, no acceptable performance should be expected.
Generative adversarial networks (GAN) are receiving a significant interest, especially in the image
analysis field due to their outstanding generative capabilities. This paper investigates whether
or not GAN can be used as an oversampling tool to compensate for an unbalanced data set in
an industrial manipulator fault diagnosis task. A comprehensive empirical analysis is performed
taking into account six different scenarios for mitigating the unbalanced data, including classical
under and oversampling (SMOTE) methods. In all of these, a wavelet packet transform is used
for feature generation while a random forest is used for fault classification. Aspects such as loss
functions, learning curves, random input distributions, data shuffling, and initial conditions were also
considered. A non-parametric statistical test of hypotheses reveals that all GAN based fault-diagnosis
outperforms both under and oversampling classical methods while, within GAN based methods,
an average accuracy difference as high as 1.68% can be achieved.
Descrição
Palavras-chave
Feature extraction Generative adversarial network Unbalance data Fault diagnosis Random forest
Contexto Educativo
Citação
Editora
MDPI