用小波变换+YOLO检测轴承故障的时频特征，提升识别精度与可解释性。
CWT-Enhanced Vibration Sensing With Time-Frequency Region Localization Using YOLO
- 将振动信号转为小波谱图，再用YOLO系列模型定位故障能量区域。
- 在三个数据集上达到最高99.5%的mAP，显著优于传统方法和STFT。
- 结果可直观关联时频能量分布与故障频率，适合工业场景中的故障诊断。
本短文提出一种基于连续小波变换（CWT）增强的振动传感框架，通过在小波谱图上局部化检测时频域内的故障相关能量区域，实现轴承故障监测。振动信号经转换为CWT谱图后，提升了弱信号与非平稳故障特征的可观测性；采用YOLOv9、YOLOv10和YOLOv11检测时频域中与故障相关的局部能量区域。在CWRU、PU和IMS数据集上的实验表明，该框架相比传统时序模型、现代视觉骨干网络及基于短时傅里叶变换（STFT）的表示方法，在故障相关感知模式的可检测性与鲁棒性方面均有提升，分别获得最高99.4%、97.8%和99.5%的平均精度均值（mAP）。此外，局部区域检测框架提供了时频能量分布与典型轴承故障频率之间的更可解释关系。结果证明了该方法在噪声工业环境下的有效性和通用性。
原文摘要 · Abstract (English)
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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