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

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.

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.

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.

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.