A Contrastive Representation Domain Adaptation Method for Industrial Time-Series Cross-Domain Prediction

Industrial time-series prediction is crucial for Industrial Internet of Things. Due to the complexity and variation of modern industry, knowledge transfer for varying data has been an attractive research area. However, conventional methods may overlook the intradomain distribution and the mutual information, leading to incorrect semantic alignment and loss of prediction-relevant information. To address these issues, a contrastive learning-based domain adaptation method, contrastive temporal prediction adaptation, for industrial time-series cross-domain prediction is proposed. It leverages a contrastive domain generalization and a contrastive self-supervised alignment method to obtain stable representations and capture the relationship between the data distribution and labels, to bring samples with similar labels closer in the feature space. Besides, an instancewise adversarial discrimination is developed to leverage the data distribution to mitigates interference from irrelevant information. The performance of our method is verified through experiments on CMAPSS dataset. The results demonstrate that our method outperforms existing methods.