Compute-conscious multi-view neural framework with efficient state space models and reduced-order cross-attention for rolling bearing fault diagnosis

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Abstract

Rolling bearing fault diagnosis under practical compute constraints and long time series remains challenging in industrial settings. This study presented a compact same-source multi-view framework that combines a multi-scale gated Mamba sequence branch, a residual network with a convolutional block attention module (ResNet-CBAM) image branch, and lightweight low-rank cross-view fusion. Continuous wavelet transform (CWT) images were used as the default image view in the main experiments because they provide the most suitably maintained same-condition representation among the evaluated image views. Under a unified three-seed protocol, the proposed model reached 99.20% on Case Western Reserve University (CWRU) and 99.61% on Southeast University (SEU) in same-condition diagnosis, and attained the strongest cross-condition results within the internal comparison, with 27.39% on CWRU and 30.43% on SEU. The model used about 5.46×10⁵ parameters and 5.64 × 10⁸ multiply–accumulate operations (MACs). These results indicate a balanced trade-off between relative cross-condition generalization and computational cost, while also showing that broader operating-condition diversity, industrial validation, and deployment-oriented central processing unit (CPU) or edge profiling remain necessary.

Keywords

bearing fault diagnosis / multi-view fusion / state space model / Mamba gating / low-rank cross-attention / deep learning

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