This research focuses on detecting data drift in bearing signals by generating feature map-based images using simulation signals. The study then applies the YOLO object detection algorithm to estimate the condition of the bearings. This approach enhances the accuracy and reliability of bearing status estimation under varying conditions.

Rotating machinery faults can cause significant industrial disruptions, but traditional sensor-based diagnostics often require invasive methods. This study introduces a vision-based diagnostic algorithm that amplifies specific frequencies using time-frequency filtering, enabling accurate fault detection through measurable displacement. The approach was validated on a rotor kit testbed, effectively identifying faults with precise displacement analysis.