Abstract: In engineering practice, the performance of fault diagnosis models is affected by factors such as strong noise interference, limited sample sizes, and high model complexity, which poses challenges to the application of existing data-driven intelligent models for equipment diagnosis. To address these issues, this paper proposes a lightweight model, PCSA-Net, based on a parallel optimized convolutional block attention module (CBAM). First, a multi-scale signal feature extractor (SFE) is used to convert the input sensor signal into a feature map. Then, the traditional CBAM is optimized through the development of a collaborative attention block, the design of a learnable layer scaling strategy, and the parallelization of perceptual data features. Additionally, a PW-Pool dimension reduction module is introduced by combining point convolution with average pooling layers to reduce the number of model parameters. The channel feature vector of the feature map is then integrated to obtain the final diagnosis result. Finally, the proposed model is validated using two datasets containing common bearing faults. Experimental results show that in the small sample bearing fault diagnosis (BFD) task, the proposed model outperforms the existing mainstream fault diagnosis framework in terms of lightness and robustness, and meets the practical needs of bearing fault detection in real-world applications.

Key words: variable operating condition fault diagnosis, convolutional neural network, attention mechanism, deep learning