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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| Other Authors: | , , , , |
| Format: | article |
| Language: | eng |
| Published: |
2020
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| Subjects: | |
| Online Access: | http://hdl.handle.net/10400.1/14898 |
| Country: | Portugal |
| Oai: | oai:sapientia.ualg.pt:10400.1/14898 |