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An Efficient IIoT-Based Smart Sensor Node for Predictive Maintenance of Induction Motors

Majida Kazmi1,*, Maria Tabasum Shoaib1,2, Arshad Aziz3, Hashim Raza Khan1,2, Saad Ahmed Qazi1,2

1 Faculty of Electrical and Computer Engineering, NED University of Engineering and Technology, Karachi, 75270, Pakistan
2 Neurocomputation Lab, National Center of Artificial Intelligence, Karachi, 75270, Pakistan
3 Department of Electrical Engineering, PNEC, National University of Sciences and Technology (NUST), Karachi, 75350, Pakistan

* Corresponding Author: Majida Kazmi. Email: email

(This article belongs to the Special Issue: Machine Learning for Industrial Internet of Things (IIoT))

Computer Systems Science and Engineering 2023, 47(1), 255-272. https://doi.org/10.32604/csse.2023.038464

Abstract

Predictive maintenance is a vital aspect of the industrial sector, and the use of Industrial Internet of Things (IIoT) sensor nodes is becoming increasingly popular for detecting motor faults and monitoring motor conditions. An integrated approach for acquiring, processing, and wirelessly transmitting a large amount of data in predictive maintenance applications remains a significant challenge. This study presents an IIoT-based sensor node for industrial motors. The sensor node is designed to acquire vibration data on the radial and axial axes of the motor and utilizes a hybrid approach for efficient data processing via edge and cloud platforms. The initial step of signal processing is performed on the node at the edge, reducing the burden on a centralized cloud for processing data from multiple sensors. The proposed architecture utilizes the lightweight Message Queue Telemetry Transport (MQTT) communication protocol for seamless data transmission from the node to the local and main brokers. The broker’s bridging allows for data backup in case of connection loss. The proposed sensor node is rigorously tested on a motor testbed in a laboratory setup and an industrial setting in a rice industry for validation, ensuring its performance and accuracy in real-world industrial environments. The data analysis and results from both testbed and industrial motors were discussed using vibration analysis for identifying faults. The proposed sensor node is a significant step towards improving the efficiency and reliability of industrial motors through real-time monitoring and early fault detection, ultimately leading to minimized unscheduled downtime and cost savings.

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Cite This Article

APA Style
Kazmi, M., Shoaib, M.T., Aziz, A., Khan, H.R., Qazi, S.A. (2023). An efficient iiot-based smart sensor node for predictive maintenance of induction motors . Computer Systems Science and Engineering, 47(1), 255-272. https://doi.org/10.32604/csse.2023.038464
Vancouver Style
Kazmi M, Shoaib MT, Aziz A, Khan HR, Qazi SA. An efficient iiot-based smart sensor node for predictive maintenance of induction motors . Comput Syst Sci Eng. 2023;47(1):255-272 https://doi.org/10.32604/csse.2023.038464
IEEE Style
M. Kazmi, M.T. Shoaib, A. Aziz, H.R. Khan, and S.A. Qazi "An Efficient IIoT-Based Smart Sensor Node for Predictive Maintenance of Induction Motors ," Comput. Syst. Sci. Eng., vol. 47, no. 1, pp. 255-272. 2023. https://doi.org/10.32604/csse.2023.038464



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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