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

Author

Listed:
  • Roman Rodriguez-Aguilar

    (Facultad de Ciencias Económicas y Empresariales, Universidad Panamericana, Mexico City 03920, Mexico)

  • Jose-Antonio Marmolejo-Saucedo

    (Facultad de Ingenieria, Universidad Nacional Autonoma de Mexico, Mexico City 04510, Mexico)

  • Utku Köse

    (Faculty of Engineering, Suleyman Demirel University, Isparta 32260, Turkey)

Abstract

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.

Suggested Citation

  • Roman Rodriguez-Aguilar & Jose-Antonio Marmolejo-Saucedo & Utku Köse, 2024. "Development of a Digital Twin Driven by a Deep Learning Model for Fault Diagnosis of Electro-Hydrostatic Actuators," Mathematics, MDPI, vol. 12(19), pages 1-17, October.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:19:p:3124-:d:1493115
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    References listed on IDEAS

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    2. Xiaohan Chen & Beike Zhang & Dong Gao, 2021. "Bearing fault diagnosis base on multi-scale CNN and LSTM model," Journal of Intelligent Manufacturing, Springer, vol. 32(4), pages 971-987, April.
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