Published on: April 2026
PREDICTIVE MAINTENANCE SYSTEM: A REVIEW
Dipanshu Rangari Sujal More Jayant Meshram
Danish Meshram
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Abstract
Electric motors are critical components in modern industrial systems, where unexpected failures can lead to costly downtime, safety risks, and reduced productivity. Traditional maintenance approaches, such as breakdown and scheduled maintenance, often prove inefficient due to reactive responses or unnecessary servicing. This project presents a hardware-based predictive maintenance system designed to enhance the reliability and efficiency of electric motors. The system continuously monitors key parameters including vibration, temperature, current, voltage, rotational speed (RPM), noise, and load conditions to assess motor health in real time. Sensors interfaced with a microcontroller collect and process data, while signal conditioning ensures measurement accuracy. The system analyzes parameter variations using predefined thresholds and pattern-based techniques to detect early signs of faults. When abnormal conditions are identified, alerts are generated to enable timely maintenance actions. This approach minimizes unplanned downtime, reduces maintenance costs, and extends motor lifespan. The proposed model demonstrates the effectiveness of multi-parameter monitoring and provides a scalable foundation for future integration with advanced analytics and Industrial Internet of Things (IIoT) technologies.
How to Cite this Paper
Rangari, D., More, S. & Meshram, J. (2026). Predictive Maintenance System: A Review. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(04). https://doi.org/10.55041/ijcope.v2i4.410
Rangari, Dipanshu, et al.. "Predictive Maintenance System: A Review." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 04, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i4.410.
Rangari, Dipanshu,Sujal More, and Jayant Meshram. "Predictive Maintenance System: A Review." International Journal of Creative and Open Research in Engineering and Management 02, no. 04 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i4.410.
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- •Peer Review Type: Double-Blind Peer Review
- •Published on: Apr 17 2026
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