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.
Keywords
Cite
@article{arxiv.2509.03070,
title = {CWT-Enhanced Vibration Sensing With Spatial Fault Localization Using YOLO},
author = {Po-Heng Chou and Wei-Lung Mao and Ru-Ping Lin and Jen-Yu Chiu and Chun-Yu Yeh},
journal= {arXiv preprint arXiv:2509.03070},
year = {2026}
}
Comments
5 pages, 3 figures, 2 tables, submitted to IEEE Sensors Letters