Improved Fault Diagnosis in Hydraulic Systems with Gated Convolutional Autoencoder and Partially Simulated Data

This paper examines the effectiveness of neural network algorithms for hydraulic system fault detection and a novel neural network architecture is suggested. The proposed gated convolutional autoencoder was trained on a simulated training set augmented with just 0.2% data from the real test bench, d...

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Detalles Bibliográficos
Autores principales: Gareev, Albert, Protsenko, Vladimir, Stadnik, Dmitriy, Greshniakov, Pavel, Yuzifovich, Yuriy, Minaev, Evgeniy, Gimadiev, Asgat, Nikonorov, Artem
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8272240/
https://www.ncbi.nlm.nih.gov/pubmed/34199115
http://dx.doi.org/10.3390/s21134410

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8272240/
https://www.ncbi.nlm.nih.gov/pubmed/34199115
http://dx.doi.org/10.3390/s21134410

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