Multi-source domain open-set deep transfer adversarial network for operating performance assessment.

The process operating performance assessment (POPA) of electro-fused magnesium furnace (EFMF) is very important to ensure product quality and pursue the maximum comprehensive economic benefit. However, the data at the beginning of the new production processes do not have performance grade labels and often includes new performance grades. Traditional multi-source domain open-set domain adaptation (OSDA) method categorizes all unknown classes into one class without further subdivision. To address this issue, a method based on multi-source domain open-set deep transfer adversarial network (MDODTAN) is studied to solve the POPA problem of the EFMF, which focuses on subdividing multiple unknown classes into different unknown performance grades. This network designs a task classifier for each source domain, and the assessment accuracy of known performance grades is further enhanced. Then, the domain gap between the known performance grades in each source-target domain is reduced through multi-source domain adversarial training. By constructing a similarity matrix between the known and unknown performance grades, pseudo-labels are assigned to the target domain data, and the assessment accuracy of performance grade of the new smelting process is improved through iterative training. The experimental results indicate that our method achieves higher performance assessment accuracy in open-set scenarios compared to existing methods, while also accurately classifying and subdividing multiple unknown performance grades.