{"id":9655283,"date":"2022-11-22T21:41:30","date_gmt":"2022-11-22T18:41:30","guid":{"rendered":"https:\/\/www.iee.ihu.gr\/?post_type=phd_students&#038;p=9655283"},"modified":"2026-03-17T13:44:19","modified_gmt":"2026-03-17T10:44:19","slug":"%ce%b3%cf%81%ce%b1%ce%b2%ce%ac%ce%bd%ce%b7%cf%82-%ce%b3%ce%b5%cf%8e%cf%81%ce%b3%ce%b9%ce%bf%cf%82","status":"publish","type":"phd_students","link":"https:\/\/www.iee.ihu.gr\/en\/phd_students\/%ce%b3%cf%81%ce%b1%ce%b2%ce%ac%ce%bd%ce%b7%cf%82-%ce%b3%ce%b5%cf%8e%cf%81%ce%b3%ce%b9%ce%bf%cf%82\/","title":{"rendered":"Gravanis Georgios"},"content":{"rendered":"<p>The scope of this Ph.D. thesis is to examine the possibility of using deep neural networks with architectures, such as those of Time Delay Neural Networks (TDNN) and Long Short-Term Memory (LSTM) for early fault detection in industrial and production processes.\u00a0 With this thesis, deep learning architectures that will make the produced models capable of use in real production time, will be developed. 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