LAUSR

To ensure the reliability and safety of modern industrial process monitoring, computer vision-based soft measurement has received considerable attention due to its nonintrusive property. State-of-the-art computer vision-based approaches mostly rely on feature embedding from deep neural networks. However, this kind of feature extraction suffers from noise effects and limitation of labeled training instances, leading to unsatisfactory performance in real industrial process monitoring. In this article, we develop a novel hybrid learning framework for feature representation based on knowledge distillation and supervised contrastive learning. First, we attempt to transfer the abundant semantic information in handcrafted features to deep learning feature-based network by knowledge distillation. Then, to enhance the feature discrimination, supervised contrastive learning is proposed to contrast many positive pairs against many negative pairs per anchor. Meanwhile, two important mechanisms, memory queue-based negative sample augmentation and hard negative sampling, are added into the supervised contrastive learning model to assist the proper selection of negative samples. Finally, a flotation process monitoring problem is considered to illustrate and demonstrate the effectiveness of the proposed method.