Improving the Performance of Convolutional GAN Using History-State Ensemble for Unsupervised Early Fault Detection with Acoustic Emission Signals

Improving the Performance of Convolutional GAN Using History-State Ensemble for Unsupervised Early Fault Detection with Acoustic Emission Signals

Early fault detection (EFD) in run-to-failure processes plays a crucial role in the condition monitoring of modern industrial rotating facilities, which entail increasing demands for safety, energy and ecological savings and efficiency. To enable effective protection measures, the evolving faults ha...

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Bibliographic Details
Main Authors: Yu Wang, Alexey Vinogradov
Format: Article
Language:English
Published: MDPI AG 2023-02-01
Series:Applied Sciences
Subjects:
early fault detection
acoustic emission signal
unsupervised learning
ensembled method
convolutional GAN
Online Access:https://www.mdpi.com/2076-3417/13/5/3136

Internet

https://www.mdpi.com/2076-3417/13/5/3136

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