Deep Learning-Based Fault Detection in Industrial Motor Systems

Abstract

Industrial motors are critical components of modern manufacturing systems, and unexpected motor failures can result in significant production downtime and maintenance costs. This study proposes a deep-learning-based approach for detecting and classifying faults in industrial motor systems. Operational signals, including vibration, current, temperature and rotational characteristics, are considered for developing an intelligent monitoring framework. A deep neural network can be trained to distinguish between normal operating conditions and different fault categories, enabling early identification of abnormal motor behavior. The proposed approach reduces dependence on conventional manual inspection and provides opportunities for continuous automated monitoring. The research also examines the importance of data quality, feature representation and model performance in developing reliable fault-detection systems. The proposed framework demonstrates how deep learning can support intelligent industrial diagnostics and contribute to improved motor reliability, reduced maintenance interruptions and enhanced overall equipment