Process Operation Performance Assessment Based on Semi-Supervised Fine-Grained Generative Adversarial Network for EFMF

The process operation performance assessment (POPA) of electro-fused magnesium furnace (EFMF) plays an irreplaceable role in improving production efficiency and ensuring product quality. The existing methods of POPA seriously rely on a large number of manually marked label data. However, manual marking of all samples will pay a very high price, which is not applicable in the actual industrial application of EFMF. Therefore, taking advantage of the multisource heterogeneous information of image, sound, and current generated by EFMF, we propose a novel semi-supervised fine-grained generative adversarial network (SSFGGAN) that addresses the issue of POPA of the EFMF smelting process. The SSFGGAN method makes use of the generative adversarial network and semi-supervised strategy and works together with the classifier, which makes the feature distribution of each operation performance grade in source domain (SD) and target domain (TD) consistent through adversarial learning of a fine-grained discriminator. Therefore, the comprehensive features learned in SD remain effective for TD data and can be directly applied to POPA of TD. Finally, the simulation results show that the assessment accuracy has been improved with a small number of labeled data and a large number of unlabeled data. The accuracy of the SSFGGAN method can reach 99.875%, 99.75%, 99.625%, and 99.125%, respectively, when the ratio of labeled data to unlabeled data is 1:2, 1:5, 1:10, and 1:20. In each evaluation index, it is better than the compared methods.