YOLO-based Bearing Fault Diagnosis With Continuous Wavelet Transform
This provides a practical solution for condition monitoring in rotating machinery, though it is incremental as it applies existing YOLO models to a new domain.
The paper tackles bearing fault diagnosis by proposing a YOLO-based framework that uses continuous wavelet transform to create time-frequency spectrograms from vibration signals, achieving high accuracy with mAP scores up to 99.5% on benchmark datasets.
This letter proposes a YOLO-based framework for spatial bearing fault diagnosis using time-frequency spectrograms derived from continuous wavelet transform (CWT). One-dimensional vibration signals are first transformed into time-frequency spectrograms using Morlet wavelets to capture transient fault signatures. These spectrograms are then processed by YOLOv9, v10, and v11 models to classify fault types. Evaluated on three benchmark datasets, including Case Western Reserve University (CWRU), Paderborn University (PU), and Intelligent Maintenance System (IMS), the proposed CWT-YOLO pipeline achieves significantly higher accuracy and generalizability than the baseline MCNN-LSTM model. Notably, YOLOv11 reaches mAP scores of 99.4% (CWRU), 97.8% (PU), and 99.5% (IMS). In addition, its region-aware detection mechanism enables direct visualization of fault locations in spectrograms, offering a practical solution for condition monitoring in rotating machinery.