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This study introduces a novel Mixture Variational Autoencoder Regression (MVAE-R) for industrial processes. The MVAE-R model effectively extracts quality-related features from multimodal data, improving soft sensor modeling and process monitoring.

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Area of Science:

  • Industrial process monitoring
  • Soft sensor modeling
  • Multimodal data analysis

Background:

  • Traditional Mixture Variational Autoencoders (MVAE) excel at feature extraction for multimodal data.
  • Unsupervised MVAE models can include irrelevant information, hindering quality variable prediction.
  • Complex industrial processes require advanced methods for accurate monitoring and control.

Purpose of the Study:

  • To propose a quality-related Mixture Variational Autoencoder Regression (MVAE-R) for enhanced soft sensor modeling.
  • To improve feature extraction by separating quality-independent and quality-related subspaces.
  • To effectively capture nonlinear features correlated with target quality variables in industrial processes.

Main Methods:

  • Developed MVAE-R by mapping process variables into quality-independent and quality-related subspaces.
  • Extracted prior information from both process and quality variables for subspace mapping.
  • Learned latent variables under each modality for feature representation and quality prediction.

Main Results:

  • Successfully separated latent variables related and unrelated to quality.
  • Effectively captured nonlinear features highly correlated with the target quality variable.
  • Demonstrated superior performance in numerical and real industrial case studies.

Conclusions:

  • The proposed MVAE-R model offers an effective approach for soft sensor modeling in complex multimodal industrial processes.
  • MVAE-R enhances feature extraction by focusing on quality-relevant information.
  • The method shows significant effectiveness and superiority compared to existing approaches.