IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i19p3124-d1493115.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/19/3124/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/12/19/3124/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Duan, Yanqing & Edwards, John S. & Dwivedi, Yogesh K, 2019. "Artificial intelligence for decision making in the era of Big Data – evolution, challenges and research agenda," International Journal of Information Management, Elsevier, vol. 48(C), pages 63-71.
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Latinovic, Zoran & Chatterjee, Sharmila C., 2022. "Achieving the promise of AI and ML in delivering economic and relational customer value in B2B," Journal of Business Research, Elsevier, vol. 144(C), pages 966-974.
    2. Robertson, Jeandri & Botha, Elsamari & Oosthuizen, Kim & Montecchi, Matteo, 2025. "Managing change when integrating artificial intelligence (AI) into the retail value chain: The AI implementation compass," Journal of Business Research, Elsevier, vol. 189(C).
    3. Fawwaz Tawfiq Awamleh & Ala Nihad Bustami, 2022. "Examine the Mediating Role of the Information Technology Capabilities on the Relationship Between Artificial Intelligence and Competitive Advantage During the COVID-19 Pandemic," SAGE Open, , vol. 12(3), pages 21582440221, August.
    4. Garcia-Murillo, Martha & MacInnes, Ian, 2023. "The promise and perils of artificial intelligence: Overcoming the odds," 32nd European Regional ITS Conference, Madrid 2023: Realising the digital decade in the European Union – Easier said than done? 277963, International Telecommunications Society (ITS).
    5. Tieli Wang & Dingliang Wang & Zhiwei Zeng, 2024. "Research on the Construction and Measurement of Digital Governance Level System of County Rural Areas in China—Empirical Analysis Based on Entropy Weight TOPSIS Model," Sustainability, MDPI, vol. 16(11), pages 1-23, May.
    6. Bao, Haixu & Liu, Wenfei & Dai, Zheng, 2025. "Artificial intelligence vs. public administrators: Public trust, efficiency, and tolerance for errors," Technological Forecasting and Social Change, Elsevier, vol. 215(C).
    7. Philip Mundlos, 2025. "The impact of artificial intelligence on managerial attention allocation for discontinuous change: a conceptual framework," Management Review Quarterly, Springer, vol. 75(2), pages 1-45, June.
    8. Kanungo, Rama Prasad & Gupta, Suraksha & Patel, Parth & Prikshat, Verma & Liu, Rui, 2022. "Digital consumption and socio-normative vulnerability," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    9. Su, Yunsheng & Shi, Luojie & Zhou, Kai & Bai, Guangxing & Wang, Zequn, 2024. "Knowledge-informed deep networks for robust fault diagnosis of rolling bearings," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    10. Zerfaß, Ansgar & Stieglitz, Stefan & Clausen, Sünje & Ziegele, Daniel & Berger, Karen, 2023. "Communications Trend Radar 2023. State revival, scarcity management, unimagination, augmented workflows & parallel worlds," Communication Insights 17, Academic Society for Management & Communication – An initiative of the Günter Thiele Foundation, Leipzig.
    11. Luca Grilli & Sergio Mariotti & Riccardo Marzano, 2024. "Artificial intelligence and shapeshifting capitalism," Journal of Evolutionary Economics, Springer, vol. 34(2), pages 303-318, April.
    12. Zhang, Yaozhi & Prebensen, Nina, 2023. "Co-creating with ChatGPT for tourism marketing materials," OSF Preprints nvbyj, Center for Open Science.
    13. Ding, Yifei & Jia, Minping & Zhuang, Jichao & Cao, Yudong & Zhao, Xiaoli & Lee, Chi-Guhn, 2023. "Deep imbalanced domain adaptation for transfer learning fault diagnosis of bearings under multiple working conditions," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    14. Feng Ji & Yonghua Zhou & Hongjian Zhang & Guiqing Cheng & Qubo Luo, 2025. "Navigating the Digital Odyssey: AI-Driven Business Models in Industry 4.0," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 16(1), pages 5714-5757, March.
    15. Varun Nayyar, 2022. "Reviewing the impact of digital migration on the consumer buying journey with robust measurement of PLS‐SEM and R Studio," Systems Research and Behavioral Science, Wiley Blackwell, vol. 39(3), pages 542-556, May.
    16. Hongquan Gui & Jialan Liu & Chi Ma & Mengyuan Li, 2024. "Industrial-oriented machine learning big data framework for temporal-spatial error prediction and control with DTSMGCN model," Journal of Intelligent Manufacturing, Springer, vol. 35(3), pages 1173-1196, March.
    17. Bosse, Douglas & Thompson, Steven & Ekman, Peter, 2023. "In consilium apparatus: Artificial intelligence, stakeholder reciprocity, and firm performance," Journal of Business Research, Elsevier, vol. 155(PA).
    18. Taiga Saito & Shivam Gupta, 2025. "Big data applications with theoretical models and social media in financial management," Annals of Operations Research, Springer, vol. 348(3), pages 1489-1511, May.
    19. Leal Filho, Walter & Wall, Tony & Rui Mucova, Serafino Afonso & Nagy, Gustavo J. & Balogun, Abdul-Lateef & Luetz, Johannes M. & Ng, Artie W. & Kovaleva, Marina & Safiul Azam, Fardous Mohammad & Alves,, 2022. "Deploying artificial intelligence for climate change adaptation," Technological Forecasting and Social Change, Elsevier, vol. 180(C).
    20. Mohammad Hossein Ronaghi, 2023. "The influence of artificial intelligence adoption on circular economy practices in manufacturing industries," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(12), pages 14355-14380, December.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:12:y:2024:i:19:p:3124-:d:1493115. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.