Digital Twin-Driven Predictive Maintenance: A Review of Induction Motor Bearing Fault Detection and Prognostics

This paper presents a comprehensive review of induction motor bearing fault prediction methods, centering around the emerging Digital Twin approach for predictive maintenance. Traditional fault prediction methods, including signal processing, machine learning, and hybrid techniques, have laid the groundwork for bearing health monitoring but often face limitations in real-time adaptability and scalability. In contrast, Digital Twin technology creates a virtual replica of the motor, integrating real-time sensor data and advanced analytics to accurately estimate the Remaining Useful Life (RUL) of bearings. By continuously updating the virtual model with operational data, the Digital Twin enhances the accuracy of fault detection and improves predictive maintenance capabilities. The integration of AI and machine learning with Digital Twin technology further boosts RUL predictions, offering potential benefits such as reduced downtime, improved reliability, and optimized maintenance schedules. This review highlights the advantages of the Digital Twin approach and suggests upcoming research prospects to expand its industrial application and improve operational efficiency.