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A SAW wireless sensor network platform for industrial predictive maintenance

Author

Listed:
  • Bérenger Ossété Gombé

    (SENSeOR SAS, Besançon
    FEMTO-ST/Time and Frequency)

  • Gwenhael Goavec Mérou

    (FEMTO-ST/Time and Frequency)

  • Karla Breschi

    (FEMTO-ST/DISC)

  • Hervé Guyennet

    (FEMTO-ST/DISC)

  • Jean-Michel Friedt

    (SENSeOR SAS, Besançon
    FEMTO-ST/Time and Frequency)

  • Violeta Felea

    (FEMTO-ST/DISC)

  • Kamal Medjaher

    (Laboratoire Génie de Production/INP-ENIT)

Abstract

Predictive maintenance predicts the system health, based on the current condition, and defines the needed maintenance activities accordingly. This way, the system is only taken out of service if direct evidence exists that deterioration has actually taken place. This increases maintenance efficiency and productivity on one hand, and decreases maintenance support costs and logistics footprints on the other. We propose a system based on wireless sensor network to monitor industrial systems in order to prevent faults and damages. The sensors use the surface acoustic wave technology with an architecture composed of an electronic interrogation device and a passive sensor (without energy at the transducer) which is powered by the radio frequency transmitted by the interrogation unit. The radio frequency link transfers energy to the sensor to perform its measurement and to transmit the result to the interrogation unit—or in a description closer to the implemented, characterize the cooperative target cross section characteristics to recover the physical quantity defining the transducer material properties. We use this sensing architecture to measure the temperature of industrial machine components and we evaluate the robustness of the method. This technology can be applied to other physical parameters to be monitored. Captured information is transmitted to the base station through multi-hop communications. We also treat interferences involved in both interrogator to interrogator and sensor to interrogator communications.

Suggested Citation

  • Bérenger Ossété Gombé & Gwenhael Goavec Mérou & Karla Breschi & Hervé Guyennet & Jean-Michel Friedt & Violeta Felea & Kamal Medjaher, 2019. "A SAW wireless sensor network platform for industrial predictive maintenance," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1617-1628, April.
    • Handle: RePEc:spr:joinma:v:30:y:2019:i:4:d:10.1007_s10845-017-1344-0
    DOI: 10.1007/s10845-017-1344-0
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    References listed on IDEAS

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    1. Hu, Chao & Youn, Byeng D. & Wang, Pingfeng & Taek Yoon, Joung, 2012. "Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life," Reliability Engineering and System Safety, Elsevier, vol. 103(C), pages 120-135.
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    Cited by:

    1. Saneh Lata Yadav & R. L. Ujjwal, 2021. "Mitigating congestion in wireless sensor networks through clustering and queue assistance: a survey," Journal of Intelligent Manufacturing, Springer, vol. 32(8), pages 2083-2098, December.

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