Tokamaks, as a leading technology in the quest for nuclear fusion energy, play a pivotal role in the fight against climate change. For tokamaks to become a viable solution for clean energy however, they must effectively detect and manage disruptions — plasma instabilities that can cause significant damages, impairing the reliability and efficiency required for their widespread adoption as a clean energy source. Machine learning (ML) models have shown promise in predicting disruptions for specific tokamaks, but they often struggle in generalizing to the diverse characteristics and dynamics of different machines. This limits the effectiveness of ML models across different tokamak designs and operating conditions, which is a critical barrier to scaling fusion technology. Given the success of data augmentation in improving model robustness and generalizability in other fields, this study explores the use of a novel time series viewmaker network to generate diverse augmentations or "views" of training data. Our results show that incorporating views during training improves AUC and F2 scores on DisruptionBench tasks compared to standard or no augmentations. This approach represents a promising step towards developing more broadly applicable ML models for disruption avoidance, which is essential for advancing fusion technology and, ultimately, addressing climate change through reliable and sustainable energy production.