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Predictive Maintenance (PdM) Structure Using Internet of Things (IoT) for Mechanical Equipment Used into Hospitals in Rwanda

Author

Listed:
  • Irene Niyonambaza

    (African Center of Excellence in Internet of Things (ACEIoT), College of Science and Technology, University of Rwanda, Kigali P.O. Box 3900, Rwanda)

  • Marco Zennaro

    (Telecommunications/ICT4D Laboratory, The Abdus Salam International Centre for Theoretical Physics, Strada Costiera, 11-I-34151 Trieste, Italy)

  • Alfred Uwitonze

    (African Center of Excellence in Internet of Things (ACEIoT), College of Science and Technology, University of Rwanda, Kigali P.O. Box 3900, Rwanda)

Abstract

The success of all industries relates to attaining the satisfaction to clients with a high level of services and productivity. The success main factor depends on the extent of maintaining their equipment. To date, the Rwandan hospitals that always have a long queue of patients that are waiting for service perform a repair after failure as common maintenance practice that may involve unplanned resources, cost, time, and completely or partially interrupt the remaining hospital activities. Aiming to reduce unplanned equipment downtime and increase their reliability, this paper proposes the Predictive Maintenance (PdM) structure while using Internet of Things (IoT) in order to predict early failure before it happens for mechanical equipment that is used in Rwandan hospitals. Because prediction relies on data, the structure design consists of a simplest developed real time data collector prototype with the purpose of collecting real time data for predictive model construction and equipment health status classification. The real time data in the form of time series have been collected from selected equipment components in King Faisal Hospital and then later used to build a proposed predictive time series model to be employed in proposed structure. The Long Short Term Memory (LSTM) Neural Network model is used to learn data and perform with an accuracy of 90% and 96% to different two selected components.

Suggested Citation

  • Irene Niyonambaza & Marco Zennaro & Alfred Uwitonze, 2020. "Predictive Maintenance (PdM) Structure Using Internet of Things (IoT) for Mechanical Equipment Used into Hospitals in Rwanda," Future Internet, MDPI, vol. 12(12), pages 1-23, December.
    • Handle: RePEc:gam:jftint:v:12:y:2020:i:12:p:224-:d:458058
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    References listed on IDEAS

    as
    1. Chae, Bongsug (Kevin), 2019. "The evolution of the Internet of Things (IoT): A computational text analysis," Telecommunications Policy, Elsevier, vol. 43(10).
    2. Vasja Roblek & Maja Meško & Alojz Krapež, 2016. "A Complex View of Industry 4.0," SAGE Open, , vol. 6(2), pages 21582440166, June.
    3. Salvatore T. March & Gary D. Scudder, 2019. "Predictive maintenance: strategic use of IT in manufacturing organizations," Information Systems Frontiers, Springer, vol. 21(2), pages 327-341, April.
    4. Salvatore T. March & Gary D. Scudder, 0. "Predictive maintenance: strategic use of IT in manufacturing organizations," Information Systems Frontiers, Springer, vol. 0, pages 1-15.
    5. Prabhakar V. Varde & Michael G. Pecht, 2018. "Prognostics and Health Management," Springer Series in Reliability Engineering, in: Risk-Based Engineering, chapter 0, pages 447-507, Springer.
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