Semisupervised Contrastive Memory Network for Industrial Process Working Condition Monitoring

Computer vision is now being used more frequently to monitor working conditions in various industries. However, labeling data for this purpose can be costly, which often leads to partially labeled datasets. To overcome this issue, there is a growing demand for semisupervised data-driven models that can utilize the abundance of unlabeled data available to improve monitoring performance. While there have been many methods developed to improve data efficiency, there has been limited focus on utilizing information from past iterations to further enhance performance. To this end, a semisupervised contrastive memory network is developed. The network guides embedding functions to map inputs to match its supporting memories learned in past iterations, and a mix-up unsupervised learning strategy, which integrates consistency regularization with mutual information, is designed to enable training of the network with unlabeled data. The experimental results show that the proposed method produces more discriminative representation and is beneficial to semisupervised learning. Notably, on froth flotation process monitoring with Inception-V3 as the backbone, it achieves 90.03% top-1 accuracy with 16% labeled data, which is comparable to the fully supervised method trained with the 100% labeled data, and largely outperforms existing semisupervised methods.