DSpace Repository

Development of a Digital Twin Driven by a Deep Learning Model for Fault Diagnosis of Electro-Hydrostatic Actuators

Show simple item record

dc.creator Marmolejo-Saucedo, Jose-Antonio
dc.creator KÖSE, Utku
dc.creator Rodriguez-Aguilar, Roman
dc.date 2024-10-01T00:00:00Z
dc.date.accessioned 2025-02-25T10:18:49Z
dc.date.available 2025-02-25T10:18:49Z
dc.identifier 27feccc8-baa5-4c97-a1a1-8dc52b35c6f6
dc.identifier 10.3390/math12193124
dc.identifier https://avesis.sdu.edu.tr/publication/details/27feccc8-baa5-4c97-a1a1-8dc52b35c6f6/oai
dc.identifier.uri http://acikerisim.sdu.edu.tr/xmlui/handle/123456789/99126
dc.description The first quarter of the 21st century has witnessed many technological innovations in various sectors. Likewise, the COVID-19 pandemic triggered the acceleration of digital transformation in organizations driven by artificial intelligence and communication technologies in Industry 4.0 and Industry 5.0. Aiming at the construction of digital twins, virtual representations of a physical system allow real-time bidirectional communication. This will allow the monitoring of operations, identification of possible failures, and decision making based on technical evidence. In this study, a fault diagnosis solution is proposed, based on the construction of a digital twin, for a cloud-based Industrial Internet of Things (IIoT) system contemplating the control of electro-hydrostatic actuators (EHAs). The system was supported by a deep learning model using Long Short-Term Memory (LSTM) networks for an effective diagnostic approach. The implemented study considers data preparation and integration and system development and application to evaluate the performance against the fault diagnosis problem. According to the results obtained, positive results are shown in the construction of the digital twin using a deep learning model for the fault diagnosis problem of an active EHA-IIoT configuration.
dc.language eng
dc.rights info:eu-repo/semantics/closedAccess
dc.title Development of a Digital Twin Driven by a Deep Learning Model for Fault Diagnosis of Electro-Hydrostatic Actuators
dc.type info:eu-repo/semantics/article


Files in this item

Files Size Format View

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record

Search DSpace


Advanced Search

Browse

My Account