An efficient lightweight multi-scale CNN framework with CBAM and SPP for bearing fault diagnosis

• Novel Lightweight multi-scale CNN with CBAM and SPP for bearing faults. • Achieves >99% accuracy with only ∼3.8M parameters. • Robust to single and compound faults across CWRU and HUST datasets. • Efficient design enables practical real-time deployment. Rolling bearing degradation produces vibration signatures that vary across operating conditions, posing challenges for reliable fault diagnosis. This study proposes an adaptive and lightweight diagnostic framework combining a Depthwise Separable Multi-Scale CNN (DSMSCNN) with Convolutional Block Attention Module (CBAM) and Spatial Pyramid Pooling (SPP) to extract fault-frequency invariant features across different mechanical domains. Wavelet-based time–frequency maps are utilized to suppress noise and preserve multi-resolution spectral characteristics. The multi-scale separable convolutions adaptively capture discriminative frequency patterns, while CBAM highlights informative spectral regions and SPP enhances scale robustness without fixed input sizes. Experiments on the CWRU and HUST bearing datasets demonstrate over 99% accuracy with significantly fewer parameters than conventional CNNs. The results confirm that the proposed DSMSCNN-CBAM-SPP framework effectively captures invariant fault-frequency features, offering a compact and adaptive solution for intelligent bearing fault diagnosis and real-time predictive maintenance in a noisy environment.