Bearings are essential components across a wide range of mechanical systems, facilitating smooth motion with minimal energy dissipation. Therefore, their maintenance and health monitoring are critical for ensuring system reliability and operational safety. With the increasing integration of advanced digital technologies into industrial processes, condition monitoring systems have become indispensable for the early detection of bearing wear and damage, allowing interconnected machines to gather vast amounts of data and convert it into actionable insights. While extensive datasets offer significant opportunities for analysis, they are often subject to noise, biases, and inconsistencies that complicate the development of accurate physical models, particularly in complex, dynamic systems.