SMAConvFormer: a fault diagnosis model for rotating machinery with high noise and variable operating conditions

Abstract In the industrial field, the reliability of rotating machinery has a key impact on production safety and operation efficiency. Current fault prediction and health management methods usually rely on task-specific models, which face significant challenges when dealing with signal characteristics with different operating conditions. The existing lightweight network models have weak anti-noise ability in noisy environments and lack of cross-condition diagnosis ability and generalization. Inspired by the Transformer-convolution neural network collaboration model, this study introduces a novel fault diagnosis model named SMAConvFormer to tackle the aforementioned challenges. First, a multi-scale channel attention embedded separable convolution is proposed to dynamically enhance the feature responses of key channels via channel attention, suppress noise-induced redundancy, and accurately capture multi-scale local receptive field features that represent the early operational stage of mechanical equipment under noisy conditions. Second, a synergistic multi-dimensional self-attention mechanism is proposed, which incorporates broadcast self-attention to model global temporal correlations, multi-scale spatial attention to capture local high-frequency impulsive features, and progressive channel self-attention to optimize channel weight allocation thereby enabling the collaborative extraction of correlation features across different frequency bands. Noise interference is effectively suppressed, and the model’s diagnostic adaptability under varying operating conditions is significantly enhanced. Final, experiments show that SMAConvFormer outperforms recent fault diagnosis methods in terms of diagnostic performance and generalization ability; in addition, the effectiveness of the proposed modules are also verified.