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A new soft sensing method based on serial-parallel GRU with self-attention mechanism for complex multi-unit

Kaixiang Peng1, Guanyao Wang2, Tie Li2

  • 1Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, University of Science and Technology Beijing, Beijing, 100083, PR China; School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, 100083, PR China.

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Summary

This study introduces a new soft sensor model (SPGRU-SA) for predicting manufacturing Key Performance Indicators (KPIs). It accurately forecasts KPIs in complex industrial processes, overcoming limitations of traditional testing methods.

Keywords:
Complex industrial processDynamic and static feature fusionGated recurrent unitSelf-attentionSoft sensor

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Area of Science:

  • Manufacturing Process Optimization
  • Artificial Intelligence in Industry
  • Soft Sensor Technology

Background:

  • Traditional Key Performance Indicator (KPI) testing is time-consuming and costly.
  • Digital transformation necessitates timely and accurate KPI prediction for manufacturing.
  • Existing methods fail to provide effective real-time production guidance.

Purpose of the Study:

  • To develop an advanced soft sensor model for online KPI prediction.
  • To address the limitations of traditional destructive testing methods.
  • To improve the efficiency and accuracy of KPI monitoring in industrial processes.

Main Methods:

  • Proposed a novel Serial-Parallel Gated Recurrent Unit with Self-Attention (SPGRU-SA) soft sensor model.
  • Employed SPGRU to extract dynamic features from multi-unit processes.
  • Utilized a self-attention mechanism to weigh static and dynamic features for correlation analysis.

Main Results:

  • The SPGRU-SA model demonstrated accurate online prediction of KPIs.
  • Effectively captured both dynamic and static process features.
  • Validated performance on hot rolling strip mill and Tennessee Eastman processes.

Conclusions:

  • SPGRU-SA accurately predicts KPIs in complex, multi-unit industrial settings.
  • The model offers a viable alternative to costly and slow traditional testing.
  • Enhances real-time decision-making and reduces manufacturing losses.