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RETRACTED ARTICLE: Economic IoT strategy: the future technology for health monitoring and diagnostic of agriculture vehicles

Author

Listed:
  • Neeraj Gupta

    (Oakland University)

  • Saurabh Gupta

    (Banasthali Vidyapith
    John Deere Co. Ltd)

  • Mahdi Khosravy

    (Osaka University)

  • Nilanjan Dey

    (Techno International New Town)

  • Nisheeth Joshi

    (Banasthali Vidyapith)

  • Rubén González Crespo

    (International University of La Rioja)

  • Nilesh Patel

    (Oakland University)

Abstract

Today’s Agriculture vehicles (AgV)s are expected to encompass mainly the three requirements of customers; economy, the use of High technology and reliability. In this manuscript, we investigate the technology solution for efficient health monitoring and diagnostic (HM&D) strategy to maximize the field efficiency and minimize the machine cost. Based on the data captured by various IoT sensors, we demonstrate the facts to shift the HM&D technology from costly sensor to economic microphone based mechanism. The adopted strategy is capable to reduce the bulky data transmission on the internet, and to increase the up-time of AgVs. We experimented on the essential red hot chili peppers system of the AgV’s backbone hydraulic system—the hydraulic filter and pump. The measurement system analysis is adopted to determine the preciseness of data captured near the considered components. The envision of the correlation between the collected data extracts significant information to draw the facts to embrace the future HM&D technology shift. Correlation between the signals captured from costly sensors and Microphone for the generated faults in hydraulic components demonstrates the effectiveness of audio to replace existing HM&D technology.

Suggested Citation

  • Neeraj Gupta & Saurabh Gupta & Mahdi Khosravy & Nilanjan Dey & Nisheeth Joshi & Rubén González Crespo & Nilesh Patel, 2021. "RETRACTED ARTICLE: Economic IoT strategy: the future technology for health monitoring and diagnostic of agriculture vehicles," Journal of Intelligent Manufacturing, Springer, vol. 32(4), pages 1117-1128, April.
    • Handle: RePEc:spr:joinma:v:32:y:2021:i:4:d:10.1007_s10845-020-01610-0
    DOI: 10.1007/s10845-020-01610-0
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    References listed on IDEAS

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    1. Edielson P. Frigieri & Carlos A. Ynoguti & Anderson P. Paiva, 2019. "Correlation analysis among audible sound emissions and machining parameters in hardened steel turning," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1753-1764, April.
    2. Botao Wu & Huijuan Wang, 2019. "A Lane Identifying Approach of the Intelligent Vehicle in Complex Condition: Intelligent Vehicle in Complex Condition," International Journal of Ambient Computing and Intelligence (IJACI), IGI Global, vol. 10(4), pages 25-44, October.
    3. Aya Hamdy Ali & Ayman Atia & Mostafa-Sami M. Mostafa, 2017. "Recognizing Driving Behavior and Road Anomaly using Smartphone Sensors," International Journal of Ambient Computing and Intelligence (IJACI), IGI Global, vol. 8(3), pages 22-37, July.
    4. Jinjiang Wang & Laibin Zhang & Lixiang Duan & Robert X. Gao, 2017. "A new paradigm of cloud-based predictive maintenance for intelligent manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 28(5), pages 1125-1137, June.
    5. Xiang T. R. Kong & Hao Luo & George Q. Huang & Xuan Yang, 2019. "Industrial wearable system: the human-centric empowering technology in Industry 4.0," Journal of Intelligent Manufacturing, Springer, vol. 30(8), pages 2853-2869, December.
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    Cited by:

    1. Chi-Ming Ho, 2023. "Research on interaction of innovation spillovers in the AI, Fin-Tech, and IoT industries: considering structural changes accelerated by COVID-19," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-29, December.

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