To ensure the safe operation of Tokamak experiments, the reliability assessment of key vacuum acquisition equipment, specifically molecular pumps, is crucial. However, limited degradation data has resulted in low accuracy of existing predictive methods. To address this challenge, a degradation data generation method for molecular pumps based on time series generative adversarial networks (TGAN) has been proposed, aimed at augmenting the dataset through generated data to enhance the accuracy and reliability of predictive models. This method innovatively combines Transformer networks with TGAN and improves the quality of the generated data by incorporating Weibull distribution. Furthermore, long short-term memory networks are utilized for degradation prediction of the generated data. Experimental results demonstrate that TGAN-Transformer can effectively generate data that meets the needs of molecular pump degradation prediction, significantly enhancing prediction accuracy and reliability, thereby providing solid support for the reliability analysis and safe operation of molecular pumps. Through comparative experiments, TGAN Transformer has improved RMSE indicators by 51%, 48%, 36%, 40%, and 30% compared to GAN, DCGAN, RCGAN, VAE, and CVAE, respectively. On the MAE index, they increased by 52%, 49%, 38%, 42%, and 33% respectively, demonstrating their effectiveness in predicting molecular pump degradation. Future research may further optimize the structure of the generation network and explore more variants of generative adversarial networks to improve the diversity and authenticity of generated data, thereby further enhancing the accuracy and reliability of degradation predictions.