Frequency-aware signal image representation and graph transformer learning for bearing fault diagnosis.

INTRODUCTION: Rolling bearing fault diagnosis is essential for predictive maintenance because bearing failures can cause unexpected downtime, increased maintenance costs, and safety risks. Conventional signal-domain and image-based approaches may not adequately capture both local frequency characteristics and global structural relationships among fault patterns. METHODS: This study proposes a frequency-aware signal image representation and graph transformer learning (FSI-GTL) framework for bearing fault diagnosis. Raw vibration signals are transformed using short-time Fourier transform (STFT), wavelet packet transform (WPT), empirical mode decomposition (EMD), and cepstral analysis. The resulting representations are adaptively fused, and discriminative frequency-domain features are used to construct graph representations. A graph neural network (GNN) captures local spectral topology, while a Transformer encoder models long-range dependencies through self-attention. RESULTS: The proposed framework achieved an average classification accuracy of 99.42% on the CWRU dataset. Cross-dataset evaluation on the Paderborn University bearing dataset achieved an average accuracy of 98.71%, demonstrating strong generalization across datasets. The framework also showed high precision, recall, and F1-score across the evaluated bearing fault categories. DISCUSSION: The results demonstrate that integrating frequency-aware signal representations, graph-based structural learning, and Transformer-based global contextual modeling can effectively improve bearing fault discrimination. The proposed FSI-GTL framework provides a promising approach for intelligent condition monitoring and predictive maintenance.