Performance grade similarity-based generalized zero-shot operating performance assessment of industrial processes with insufficient samples

• Published in: Journal of process control Vol. 154; p. 103523
• Main Authors: Wang, Siqi, Liu, Yan, Fu, Lulu, Chu, Fei, Wang, Fuli, Bao, Chenhui
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.10.2025
• Subjects: Auxiliary set; Generalized zero-shot learning; Insufficient seen samples; Performance grade similarity matrix; Process operating performance assessment; Auxiliary set; Process operating performance assessment; Generalized zero-shot learning; Performance grade similarity matrix; Insufficient seen samples
• ISSN: 0959-1524
• DOI: 10.1016/j.jprocont.2025.103523

The process operating performance assessment (POPA) is vital for enhancing economic production in industrial processes. This study addresses the challenge in POPA of assessment unseen performance grades with zero samples, while also dealing with insufficient data for seen performance grades. We propose PGSGZSIS, a performance grade similarity-based generalized zero-shot method that integrates accessible superficial expert knowledge with a multi-expert voting mechanism to construct a performance grade similarity matrix (PGSM). The PGSM is validated by seen-data-driven expert reliability calculation, reducing dependency on deep expert knowledge while enhancing objectivity through data quantification. Additionally, an auxiliary set augmentation strategy based on feature similarity is introduced, constructing an auxiliary dataset by screening samples from similar operational conditions to address scarce seen samples. By constructing the PGSM and augmenting seen samples with auxiliary data, our approach not only alleviates the issue of insufficient seen samples but also tackles the generalized zero-shot learning (GZSL) problem for POPA. Experimental results validate the effectiveness of the proposed method in a hydrometallurgical process. •Proposes a novel performance grade similarity-based generalized zero-shot learning framework for scenarios with insufficient seen samples.•Introduces auxiliary sets, using similar operating condition samples to augment training data and overcome sample scarcity.•Uses accessible superficial expert knowledge and multi-expert voting to define performance grade similarity, reducing reliance on deep domain expertise.