A Global Adversarial and Local Contrastive Transfer Learning Approach for Remaining Useful Life Prediction

The Transfer learning method can effectively mitigate the reliance on an extensive amount of target domain data for the construction of remaining useful life (RUL) prediction models. While transfer learning has achieved remarkable achievements in numerous real-world applications, traditional methods often focus on aligning global domain features and extracting domain-invariant features, overlooking local and channel-specific characteristics. This limitation can result in suboptimal RUL predictions, as critical degradation-related features may be ignored. To address these challenges, this article proposes a global adversarial and local contrastive (GALC) transfer learning approach for predicting RUL in multiple domains. The proposed method employs adversarial learning to capture global domain-invariant features while incorporating channel-level and temporal-level contrastive modules to preserve local temporal patterns and channel uniqueness. This approach enables us to achieve domain-invariant feature learning while preserving channel distinctiveness across diverse domains, and its effectiveness has been validated using real-world datasets.