Integrated SVM Based on Adaptive Inertia Weight Particle Swarm Optimization Method for Si3N4 Bearing Roller Surface Defect Classification

Si3N4 bearing roller surface defects are characterized by complex gray-scale texture features and diverse morphology. An integrated support vector machine (SVM) classification method based on adaptive inertia weight particle swarm optimization (AIW-PSO) is proposed in this paper to achieve comprehensive classification of Si3N4 bearing roller surface defect images. By analyzing the complex feature information of these defect images, an ensemble SVM-based classification model is designed. To realize high-precision classification, the model’s prediction accuracy is improved through integrated learning. The AIW-PSO method effectively avoids underfitting during model training by optimizing SVM hyperparameters. The method significantly realizes the accurate classification of Si3N4 bearing roller surface defect images and greatly improves the performance of the classification model.