CWT-Enhanced Vibration Sensing With Spatial Fault Localization Using YOLO

Abstract

This letter presents a CWT-enhanced vibration sensing framework for bearing fault monitoring through spatial localization on time-frequency spectrograms. Vibration signals are transformed into continuous wavelet transform (CWT) spectrograms to improve the observability of weak and non-stationary fault signatures, and YOLOv9, YOLOv10, and YOLOv11 are employed to localize and identify fault-related energy regions. Experiments on the CWRU, PU, and IMS datasets show that the proposed framework improves the detectability and robustness of fault-related sensing patterns compared with conventional time-series models, modern vision backbones, and short-time Fourier transform (STFT)-based representations, achieving mAP values up to 99.4%, 97.8%, and 99.5%, respectively. In addition, the region-aware localization provides a more interpretable connection between time-frequency energy distributions and bearing fault characteristics. These results demonstrate that spatial localization on CWT spectrograms offers an effective and generalizable approach for enhancing vibration sensing capability in non-stationary environments.