Abdo, Ali (Birzeit University), yazan, hakawati (Birzeit University), Atarri, Abdulrahman (Birzeit University), Qadora, Aseel (Birzeit University)

Real-Time Fault Diagnosis of Single-Phasing on Three-Phase Induction Motor Based on Artificial Intelligence

Scheduled for presentation during the Regular Session "Fault diagnosis I" (WeAC), Wednesday, June 11, 2025, 11:10−11:30, Room B

33rd Mediterranean Conference on Control and Automation, June 10-13, 2025, Tangier, Morocco

This information is tentative and subject to change. Compiled on May 9, 2025

Abstract

This paper develops a real-time data-driven approach to fault diagnosis on three three-phase induction motors, offering unmatched scalability, accuracy, and flexibility. To improve feature representation, extensive datasets were gathered under various load circumstances using vibration, temperature, and current sensors. These datasets were then carefully preprocessed and examined. With a real-time classification accuracy of 99.3%, the XGBoost algorithm; which was chosen for its interpretability and robustness, performed better across unbalanced datasets. By fusing advanced machine learning with hardware that has limited resources, this study pushes the limits of predictive maintenance. It creates a scalable platform for implementing intelligent diagnostic solutions across a range of industrial applications in addition to reducing downtime and operational disturbances. The results have the potential to revolutionize companies striving for safer, smarter, and more sustainable operations in the context of Industry 4.0.