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arXiv · 2509.03070

CWT-Enhanced Vibration Sensing With Time-Frequency Region Localization Using YOLO

Po-Heng Chou·Wei-Lung Mao·Ru-Ping Lin·Jen-Yu Chiu·Chun-Yu Yeh
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Abstract

This letter presents a CWT-enhanced vibration sensing framework for bearing fault monitoring through localized time-frequency region detection on continuous wavelet transform (CWT) spectrograms. Vibration signals are transformed into CWT spectrograms to improve the observability of weak and non-stationary fault signatures, and YOLOv9, YOLOv10, and YOLOv11 are employed to detect and identify localized fault-related energy regions in the time-frequency domain. 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 mean average precision (mAP) values up to 99.4%, 97.8%, and 99.5%, respectively. In addition, the localized region detection framework provides a more interpretable relationship between time-frequency energy distributions and characteristic bearing fault frequencies. These results demonstrate an effective and generalizable approach for interpretable vibration sensing in noisy industrial environments.

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Po-Heng Chou, Wei-Lung Mao, Ru-Ping Lin, Jen-Yu Chiu, Chun-Yu Yeh. 2025-09-03. CWT-Enhanced Vibration Sensing With Time-Frequency Region Localization Using YOLO. https://arxiv.org/abs/2509.03070

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