Open Access

ARTICLE

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

Majida Kazmi^1,*, Maria Tabasum Shoaib^1,2, Arshad Aziz³, Hashim Raza Khan^1,2, Saad Ahmed Qazi^1,2

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:

(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

Received 13 December 2022; Accepted 17 February 2023; Issue published 26 May 2023

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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Keywords

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