Contrastive Learning-Based Secure Unsupervised Domain Adaptation Framework and its Application in Cross-Factory Intelligent Manufacturing

Abstract

Machine learning has been widely applied in industrial intelligent manufacturing. However, significant domain differences in data across factories make it difficult for models trained on a single factory dataset to achieve cross-factory reuse. Unsupervised Domain Adaptation is a method to address this issue, but its basic assumption is the source domain data is available. With increasing attention to data and internet security in the modern manufacturing industry, privacy protection of source data makes it unavailable. To address this challenge, we propose a contrastive learning-based secure unsupervised domain adaptation framework, which does not require source domain data and can achieve high-precision domain alignment by relying on the source domain well-trained model and the target domain unlabeled data. We conduct sufficient experimental studies on a digital recognition benchmark transfer task and a real industrial case, demonstrating that the proposed method outperforms state-of-the-art methods in terms of performance. It is worth mentioning that the proposed method can eliminate the dependence on source domain data, effectively ensuring cross-factory data privacy protection and providing new possibilities for intelligent networked collaborative manufacturing.