Real-Time Monitoring for Hydraulic States Based on Convolutional Bidirectional LSTM with Attention Mechanism

By monitoring a hydraulic system using artificial intelligence, we can detect anomalous data in a manufacturing workshop. In addition, by analyzing the anomalous data, we can diagnose faults and prevent failures. However, artificial intelligence, especially deep learning, needs to learn much data, a...

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Detalles Bibliográficos
Autores principales: Kim, Kyutae, Jeong, Jongpil
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7764528/
https://www.ncbi.nlm.nih.gov/pubmed/33322319
http://dx.doi.org/10.3390/s20247099

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7764528/
https://www.ncbi.nlm.nih.gov/pubmed/33322319
http://dx.doi.org/10.3390/s20247099

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