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Deep learning is widely used in Bearing Fault Diagnosis (BFD). Nonetheless, practical industrial production often generates a large amount of industrial noise. These noises exhibit randomness and complexity, which puts forward higher requirements for diagnosis algorithms. Certain studies have tackled the issue of anti-interference in high-noise environments (S N R ≤ 0 dB) by increasing the complexity of the model. However, due to the excessive number of parameters and computation, such models cannot be deployed on low-end edge devices. Balancing resource consumption and accuracy has become a major challenge in BFD modeling research. To solve the above problems, this paper proposes a new Transformer architecture model called LTFAFormer. The LTFAFormer is capable of achieving high-precision diagnostics on low-end edge devices and shows greater noise resistance. In terms of processing sequence information, Transformer has proven to be superior to other solutions. However, when dealing with longer sensor signal data containing complex noise, the traditional self-attention mechanism not only cannot effectively extract fault features, but also generates more computational complexity than CNN. To address this issue, we propose a novel time–frequency dual-channel parallel attention mechanism. Our approach enhances the feature extraction capability of the model by expanding the attention computation scale and reduces the computational resource consumption of the model by optimizing the model structure. To validate the effectiveness of LTFAFormer, we present two cases to demonstrate that LTFAFormer has higher prediction accuracy while satisfying lightweight. Especially in high-noise environments, LTFAFormer has stronger robustness. In this paper provides a new set of feasible strategies for the practical deployment of BFD models in practical industrial environments. • The LTFAFormer, a lightweight and noise-robust model, is ideal for low-end devices. • Added a Dual-Channel Attention Module to expand traditional self-attention scale. • Developed a Cross-fusion mechanism for attention to enhance feature fusion. • Optimized the model's embedding module based on sensor signal traits. • Our model outperforms others in bearing fault diagnosis and is robust in noise. [Display omitted] [ABSTRACT FROM AUTHOR]

• The SAM was first applied to domain-adaptive semantic segmentation. A new semantic segmentation method for mechanical assembly was proposed, and a U-SAM framework was designed to extract shallow features and improve convergence efficiency. • A strategy of replacing regular boxes with oblique boxes and an iterative loop strategy was proposed to address the prompt issue in SAM. • A line discriminator module and a trainable line-guided filtering module were proposed to further improve the accuracy of edge segmentation. Semantic segmentation of assembly images can effectively identify and prevent non-standard behaviors such as wrong assembly and missing assembly in the assembly process. However, supervised methods require massive training samples and are time-consuming to label. Unsupervised domain adaptation methods can address this issue without labeling the target data. Recently, the large-scale Segment Anything Model (SAM) has achieved significant success in semantic segmentation owing to its powerful feature extraction and instant interaction capabilities. Based on this, this paper proposed a new framework called U-SAM for unsupervised domain-adaptive semantic segmentation. Compared to other frameworks, U-SAM can obtain more segmented features while maintaining shallower features. Meanwhile, this study proposed an iterative loop strategy for the prompt module in U-SAM. This strategy gradually improves the accuracy of target prompt labels and solves the problem of no prompt labels in the target data. Additionally, a novel method of using oblique boxes instead of regular boxes was developed to achieve higher box prompt quality. Finally, a line discriminator module and a trainable line-guided filtering module were designed to improve the quality of edge segmentation. The experimental results indicate that the U-SAM network framework achieves a Dice accuracy of 84.4% for semantic segmentation on the mechanical assembly image dataset. This confirmed that our proposed method can be applied to the assembly monitoring process of target data. [ABSTRACT FROM AUTHOR]