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Multiple Industrial Induction Motors Fault Diagnosis Model within Powerline System Based on Wireless Sensor Network

Author

Listed:
  • Saud Altaf

    (University Institute of Information Technology, Pir Mehr Ali Shah Arid Agriculture University, Rawalpindi 46300, Pakistan)

  • Shafiq Ahmad

    (Industrial Engineering Department, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia)

  • Mazen Zaindin

    (Department of Statistics and Operations Research, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia)

  • Shamsul Huda

    (School of Information Technology, Deakin University, Burwood, VIC 3128, Australia)

  • Sofia Iqbal

    (Space and Upper Atmosphere Research Commission, Islamabad 44000, Pakistan)

  • Muhammad Waseem Soomro

    (School of Professional Engineering, Manukau Institute of Technology, Auckland 2023, New Zealand)

Abstract

The voltage supply of induction motors of various sizes is typically provided by a shared power bus in an industrial production powerline network. A single motor’s dynamic behavior produces a signal that travels along the powerline. Powerline networks are efficient at transmitting and receiving signals. This could be an indication that there is a problem with the motor down immediately from its location. It is possible for the consolidated network signal to become confusing. A mathematical model is used to measure and determine the possible known routing of various signals in an electricity network based on attenuation and estimate the relationship between sensor signals and known fault patterns. A laboratory WSN based induction motors testbed setup was developed using Xbee devices and microcontroller along with the variety of different-sized motors to verify the progression of faulty signals and identify the type of fault. These motors were connected in parallel to the main powerline through this architecture, which provided an excellent concept for an industrial multi-motor network modeling lab setup. A method for the extraction of Xbee node-level features has been developed, and it can be applied to a variety of datasets. The accuracy of the real-time data capture is demonstrated to be very close data analyses between simulation and testbed measurements. Experimental results show a comparison between manual data gathering and capturing Xbee sensor nodes to validate the methodology’s applicability and accuracy in locating the faulty motor within the power network.

Suggested Citation

  • Saud Altaf & Shafiq Ahmad & Mazen Zaindin & Shamsul Huda & Sofia Iqbal & Muhammad Waseem Soomro, 2022. "Multiple Industrial Induction Motors Fault Diagnosis Model within Powerline System Based on Wireless Sensor Network," Sustainability, MDPI, vol. 14(16), pages 1-29, August.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:16:p:10079-:d:888273
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    References listed on IDEAS

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    1. Bennett, Christopher J. & Stewart, Rodney A. & Lu, Jun Wei, 2015. "Development of a three-phase battery energy storage scheduling and operation system for low voltage distribution networks," Applied Energy, Elsevier, vol. 146(C), pages 122-134.
    2. Kai Ding & Chen Yao & Yifan Li & Qinglong Hao & Yaqiong Lv & Zengrui Huang, 2022. "A Review on Fault Diagnosis Technology of Key Components in Cold Ironing System," Sustainability, MDPI, vol. 14(10), pages 1-28, May.
    3. Seong-Hwan Im & Bon-Gwan Gu, 2022. "Study of Induction Motor Inter-Turn Fault Part II: Online Model-Based Fault Diagnosis Method," Energies, MDPI, vol. 15(3), pages 1-15, January.
    4. Zeki Murat Çınar & Abubakar Abdussalam Nuhu & Qasim Zeeshan & Orhan Korhan & Mohammed Asmael & Babak Safaei, 2020. "Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0," Sustainability, MDPI, vol. 12(19), pages 1-42, October.
    5. Meng-Hui Wang & Shiue-Der Lu & Cheng-Che Hsieh & Chun-Chun Hung, 2022. "Fault Detection of Wind Turbine Blades Using Multi-Channel CNN," Sustainability, MDPI, vol. 14(3), pages 1-17, February.
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