Machine Learning Based Efficient Framework for Industrial Motor Condition Monitoring

Abstract

Industrial motors play a crucial role in modern manufacturing and energy systems, where unexpected failures can result in significant downtime and financial losses. Therefore, effective condition monitoring is essential to ensure operational reliability and cost efficiency. This study presents a machine learning–based approach for industrial motor condition monitoring, capable of accurately detecting faults and predicting potential failures at an early stage. The proposed method leverages features extracted from vibration, current, and temperature data, followed by optimized classification using supervised machine learning algorithms. Experimental results demonstrate that the approach achieves high accuracy while reducing computational costs, outperforming conventional techniques. Furthermore, the system is scalable and adaptable to various motor types and operating conditions, making it suitable for real-time monitoring applications. Overall, this work contributes to improving industrial motor reliability, reducing maintenance costs, and advancing predictive maintenance strategies.