Wear prediction of internal combustion engine cylinder liners based on adaptive generative adversarial networks

• A prediction model for wear of cylinder liners is proposed based on adaptive generative adversarial network. • A dynamic noise function integrated with standard deviation interpolation is introduced to the generator. • A cosine-annealing learning rate scheduling strategy is adopted to mitigate overfitting and underfitting risks. • The DA-GAN method can be adopted to predict the wear of the cylinder liner of an internal combustion engine when only a small amount of wear and time data are given. The cylinder liner is an important component of internal combustion engine, and its wear directly affects the performance and service life of the engines. Smaller cylinder liner wear data reduces the accuracy of wear evaluation, making it difficult to effectively monitor its operational condition. To address this issue, a prediction model for wear of cylinder liners is proposed based on adaptive generative adversarial network (DA-GAN), and a small-sample data enhancement method is introduced into the model. To strengthen temporal feature extraction, a DA integrated with standard deviation interpolation is introduced to the generator, enabling real-time updates of wear volume standard deviations through adaptive noise mechanisms. A cosine-annealing learning rate scheduling strategy is further adopted to mitigate overfitting and underfitting risks by dynamically adjusting optimization trajectories during training. The results demonstrate that the DA-GAN model outperforms conventional long short-term memory neural network and synthetic minority over-sampling technique-based methods, and the determination coefficient (R²), mean square error (MSE), mean absolute error of cylinder liner wear obtained by the proposed model are 0.9682, 0.0368, and 0.1653, respectively. The framework effectively addresses the limitations of traditional approaches when handling complex environmental variables and sparse datasets, establishing a novel technical pathway for health management and intelligent maintenance of internal combustion engines.