Feature Importance Evaluation-Based Set Transformer and KAN for Steel Plate Fault Detection

Steel plate fault detection is a knotty task in the manufacturing industry. Existing methods have achieved satisfactory performance, however, they still face challenges in accuracy, generalization ability, and capturing important features. Therefore, this study designs a steel plate fault detection method based on an Improved Set transformer and Kolmogorov-Arnold Network (ISet transformer-KAN). Firstly, a feature importance evaluation mechanism is incorporated into the Set transformer, dynamically optimizing seed vectors according to input data to better capture fault-related features. This modification improves information aggregation while leveraging the Set transformer’s inherent permutation invariance, ensuring stability regardless of feature order. Furthermore, KAN employs learnable activation functions with grid expansion mechanisms to progressively capture subtle feature details in steel plate faults, improving generalization capability and detection accuracy. Finally, to further validate the effectiveness of the proposed method, comprehensive experiments were conducted using the Steel Plate Faults dataset from the UCI Machine Learning Repository. The experimental results demonstrate that the proposed method outperforms traditional machine learning methods by an average of 10%-15% in accuracy and modern deep learning approaches by an average of 2%-3% in accuracy. The implementation code supporting the findings of this study is available at https://github.com/123fggv/maincode.