Machine Learning-Based Motor Fault Detection and Predictive Analysis System

Abstract

This paper is about the design and development of a Motor Fault Detection and Prediction System that uses Artificial Intelligence and Machine Learning. Electric motors are used a lot in industries. When they fail suddenly it can cause a lot of problems like production loss, high maintenance costs and safety issues. The old ways of finding faults in motors are not very good because they need people to check the motors by hand or they use a system that just looks at limits, which may not find faults early. The system we are talking about uses sensors to monitor the condition of the motor. It looks at things like temperature, vibration, current, voltage and how fast the motor is spinning. It then uses machine learning to find out if the motor is working normally or not. The data we collect is looked at closely to find out if the motor is healthy or not and if it is not what is wrong with it like if it\'s too hot if it is overloaded if there is a problem, with the bearing if it is not working smoothly or if it has stopped working. We also use a model that can predict when a fault might happen so we can fix the motor before it breaks down completely. This system is good because it makes the motors more reliable reduces the time they are not working and helps us fix problems before they happen. The system we are talking about is also cheap can be used in places and is a smart way to monitor the condition of motors and predict faults.

Introduction

Electric motors are widely used in industrial applications, but faults can lead to major losses, delays, and equipment damage. Traditional diagnostic methods rely on manual inspection and fixed thresholds, which often fail to detect early-stage faults. To overcome this, the proposed system introduces an AI and machine learning-based motor fault detection and prediction framework for real-time condition monitoring and predictive maintenance.

The system integrates sensors (temperature, current, vibration, RPM) with a microcontroller to continuously monitor motor health. Collected data is processed and analyzed using machine learning models such as Decision Trees, Random Forest, SVM, and KNN to detect abnormal behavior and predict future faults. This enables early intervention and reduces reliance on manual diagnosis.

The architecture includes both hardware (sensors, motor, controller, power supply) and software components (Python, data processing libraries, visualization tools, and ML frameworks). Data is collected, preprocessed, stored, and used for training predictive models.

Overall, the proposed system provides a cost-effective, intelligent solution for real-time monitoring, fault detection, and predictive maintenance, improving reliability and efficiency in industrial motor operations.

References

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Copyright

Copyright © 2026 Dr. A. P. Khandait, Prof. P. A. Tadas, Samir G. Mangar, Aniket M. Sakhare, Kamlesh P. Drugkar, Sarthak S. Jogekar . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.