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INTERNATIONAL JOURNAL OF RECENT TRENDS IN TECHNOLOGY AND ENGINEERING (IJRTTE)

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TinyML-Powered Edge Intelligence FOR Real-Time Predictive Maintenance of Industrial Motors

Soundararjan. K
Professor, Annai Mathammal Sheela Engineering College, India.
Manoj Govindaraj
Associate Professor, Department of management studies, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, India.
M. Maria Sampoornam
Assistant Professor, Department of Information Technology, J.J.College of Engineering and Technology, India.

Keywords: TinyML, Edge Intelligence, Predictive Maintenance, Industrial Motors, Real Time Monitoring, On-Device Machine Learning, Condition-Based Maintenance, Industry 4.0, Low-Power AI, Smart Manufacturing.

Abstract

The increasing complexity of industrial machines and the necessity for continuous production have emphasized the importance of smart, effective, and real-time predictive maintenance systems. This paper proposes an edge-intelligence model based on TinyML to detect, and prediction industrial motor faults with high accuracy and low latency. In contrast to conventional cloud-based solutions, it can port machine learning models onto microcontrollers and edge devices in a lightweight manner such that the inference (on-device) becomes possible with ultra-low power with a much lower dependence on the network. The platform provides for real-time data collection, analysis of vibration and temperature signals, and detection of anomalies, to schedule maintenance when required, before any critical fault occurs. Extensive experiments on benchmark motor datasets show that our system is practical under various operation scenarios by improving the response time, energy consumption, and deployment scalability. This is an approach that not only overcomes the limitations associated with conventional predictive maintenance frameworks when it comes to latency, cost, scalability but can be seen as a viable and sustainable solution to the Issue in many industries 4.0 environments.
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Published
2025-06-30
Pages
1-11
Issue
Vol. 4 No. 2 (2025): IJRTTE - 04 - 02
Section
Articles

References

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