Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.03070 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913043757137920 |
|---|---|
| author | Chou, Po-Heng Mao, Wei-Lung Lin, Ru-Ping Chiu, Jen-Yu Yeh, Chun-Yu |
| author_facet | Chou, Po-Heng Mao, Wei-Lung Lin, Ru-Ping Chiu, Jen-Yu Yeh, Chun-Yu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_03070 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CWT-Enhanced Vibration Sensing With Spatial Fault Localization Using YOLO Chou, Po-Heng Mao, Wei-Lung Lin, Ru-Ping Chiu, Jen-Yu Yeh, Chun-Yu Signal Processing Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning Image and Video Processing 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. |
| title | CWT-Enhanced Vibration Sensing With Spatial Fault Localization Using YOLO |
| topic | Signal Processing Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning Image and Video Processing |
| url | https://arxiv.org/abs/2509.03070 |