YOLO-based Bearing Fault Diagnosis With Continuous Wavelet Transform

Abstract

This letter presents a locality-aware bearing fault diagnosis framework that operates on time-frequency representations and enables spatially interpretable decision-making. One-dimensional vibration signals are first mapped to two-dimensional time-frequency spectrograms using the continuous wavelet transform (CWT) with Morlet wavelets to enhance transient fault signatures. The diagnosis task is then formulated as object detection on the time-frequency plane, where YOLOv9, YOLOv10, and YOLOv11 are employed to localize fault-relevant regions and classify fault types simultaneously. Experiments on three public benchmarks, including Case Western Reserve University (CWRU), Paderborn University (PU), and Intelligent Maintenance System (IMS), demonstrate strong cross-dataset generalization compared with a representative MCNN-LSTM baseline. In particular, YOLOv11 achieves [email protected] of 99.0% (CWRU), 97.8% (PU), and 99.5% (IMS), while providing region-aware visualization of fault patterns in the time-frequency domain. These results suggest that detection-based inference on CWT spectrograms provides an effective and interpretable complementary approach to conventional global classification for rotating machinery condition monitoring.