## 4 Conclusions  
In this paper, a new relational knowledge distillation model framework network RKD-SARSN was proposed for the real-time classification of the sample data of the surface defects of strip steel. Through comparative experiments, the comprehensive performance of the proposed algorithms was found to be better than that of other classification algorithms, which can ensure classification accuracy and meet the real-time requirements of the classification of strip steel defects in the industrial field. The conclusions of the experiment are as follows:  
* Using the Cycle GAN data enhancement method can realize cross-domain migration of defective samples of strip steel and solve the problem of few defective samples in a new production line;
* The introduction of the attention mechanism and self-adaptive directional derivative threshold in the SARSN model is the key to improving the classification accuracy of fine-grained defect images;
* In the training process of the model, the self-adaptive loss function balances the differences between classes through a separate class processing mechanism, which is helpful in solving the imbalance problem of strip steel defect samples;
* Structured relational knowledge distillation can transfer the generalization performance of large complex networks to small lightweight networks, reduce the complexity of model calculation and improve the efficiency of model deployment.  
However, the method proposed in this paper may have limitations, which need to be ameliorated in future steps:  
Figure 20: The evaluation system for surface defects of strip steel.  
* The proposed self-adaptive loss function solves the problem of imbalanced samples between classes, but the sensitivity to highly imbalanced samples is average, and research in this area may need to be strengthened in the future;
* The method of structured relational knowledge distillation is outstanding in improving the detection efficiency of fine-grained image classification tasks, and it is beneficial to deploy it in industrial fields to solve practical problems. However, this method is rarely used in the fault diagnosis of mechanical vibration noise; thus, improving the fault diagnosis efficiency of vibration noise may be a future research direction.  
All authors contributed to the study conception and design. Z.S.: preliminary investigation, methodology, writing--original draft. X.H. and C.J.: verification, writing--review and editing, supervision. Y.Z. and L.Y.: project management and collecting documents, modifying formats, reference materials. All authors have read and agreed to the published version of the manuscript.  
The state key laboratory open project of China National Heavy Machinery Research Institute. The Natural Science Basis Research Plan in Shaanxi Province of China (Program No.2022]Q-568). Scientific Research Program Funded by Shaanxi Provincial Education Department (Program No.21 JK0661). Young Talent Fund of Association for Science and Technology in Shaanxi, China (Program No.20220133). Xi'an Science and Technology Plan Project (Program No.22 GXFW0041). Shaanxi Province Innovative Talent Promotion Plant (2022 KJXX-41). The Open Project of State Key Laboratory of Metal Extrusion and Forging Equipment Technology (No.S2208100.W03).  
Not applicable.  
The authors declare no conflict of interest.