Exploiting Generative Adversarial Networks as an Oversampling Method for Fault Diagnosis of an Industrial Robotic Manipulator

Exploiting Generative Adversarial Networks as an Oversampling Method for Fault Diagnosis of an Industrial Robotic Manipulator

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 enou...

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Bibliographic Details
Main Authors: Ziqiang Pu, Diego Cabrera, René-Vinicio Sánchez, Mariela Cerrada, Chuan Li, José Valente de Oliveira
Format: Article
Language:English
Published: MDPI AG 2020-10-01
Series:Applied Sciences
Subjects:
feature extraction
generative adversarial network
random forest
unbalance data
fault diagnosis
Online Access:https://www.mdpi.com/2076-3417/10/21/7712

Internet

https://www.mdpi.com/2076-3417/10/21/7712

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