Predicting the degree of rubber rupture damage using a GAN-enhanced Bayesian-optimized 1DCNN network

Abstract

Influenced by factors such as temperature, aging, and overloading, rubber bearings may undergo rupture during prolonged usage, leading to severe consequences such as bearing failure and structural damage. To accurately assess the degree of rubber rupture damage, this study proposed a novel generative adversarial network (GAN)-enhanced Bayesian-optimized one-dimensional convolutional neural network (1DCNN) framework (GAN-BCNN). In the GAN-BCNN framework, GAN is used to enhance the proportion of damaged data in the database, while Bayesian optimization is utilized to optimize crucial hyperparameters in the 1DCNN network. The predictive results indicate that the proposed GAN-BCNN model accurately predicts the degree of rubber rupture damage, achieving high classification accuracies of 99.1, 95.5, 92.9, and 98.0% for the categories of no damage, slight damage, moderate damage, and severe damage, respectively. When compared to the linear discriminant analysis model and the BCNN model without GAN enhancement, the proposed GAN-BCNN model exhibits a significant improvement in predictive accuracy, with an increase in Accuracy values of 52.4 and 6.0%, respectively. These results indicate that the GAN-BCNN model holds promising practical applications, and the GAN algorithm contributes to improving the prediction accuracy of the model.