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#5517. Concurrent analytics of temporal information and local correlation for meticulous quality prediction of industrial processes

August 2026	publication date
Proposal available till	20-05-2025
4 total number of authors per manuscript	0 $

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Journal’s subject area:

Modeling and Simulation; Industrial and Manufacturing Engineering; Computer Science Applications; Control and Systems Engineering;

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Abstract:
Many conventional quality prediction models are directly developed based on the easy-to-measure variables, and thus the local information within individual unit may be buried by information of other units. In this study, a cascaded regression network (RegNet) is proposed to solve the aforementioned issue. Specifically, the features which are adopted to develop RegNet model are extracted in two steps, including variable-wise and unit-wise feature extractions. In variable-wise feature extraction, several adjacent variables and their corresponding time lags are integrated using convolutional filter. By this means, both local correlation and temporal information within each unit can be preserved. In the unit-wise feature extraction, the local information of each unit is adopted to further explore the global correlation between different operation units. Based on the obtained global features, a fully connected layer is designed to calculate the regression weight of the quality prediction model.
Keywords:
Cascaded regression network; Convolutional filter; Local correlation and temporal information; Quality prediction model

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