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M2D-VAE: Self-Supervised Probabilistic Temporal-Spatial Latent Representation Learning for Unsupervised Industrial

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    This study introduces a novel self-supervised learning model to extract temporal-spatial latent variables from industrial data with missing values. This approach enhances industrial operational applications like data imputation and process monitoring.

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    Area of Science:

    • Industrial Process Monitoring
    • Data Science
    • Machine Learning

    Background:

    • Industrial process data frequently contain missing values due to sensor malfunctions and transmission errors.
    • Missing data hinder data-driven approaches for analyzing temporal-spatial correlations, impacting downstream industrial applications.
    • Existing methods struggle to effectively extract meaningful representations from incomplete sequential industrial data.

    Purpose of the Study:

    • To propose a self-supervised representation learning model for extracting probabilistic temporal-spatial latent variables (LVs) from industrial data with missing values.
    • To develop a unified framework for utilizing these LVs in various industrial operational applications.
    • To demonstrate the model's effectiveness in unsupervised industrial tasks, including missing value imputation and dynamic process monitoring.

    Main Methods:

    • Developed a novel deep dynamic probabilistic latent variable model, Markov dynamic variational autoencoder (MD-VAE), to capture temporal-spatial dependencies.
    • Introduced a self-supervised learning approach, masked MD-VAE, to handle missing value interference during LV extraction.
    • Incorporated Bayesian smoothing for latent posteriors and controllable constraints for model optimization.

    Main Results:

    • The proposed masked MD-VAE model successfully extracts temporal-spatial LVs from industrial data with missing values.
    • The extracted LVs were effectively utilized in a unified framework for downstream industrial tasks.
    • Case studies on a multiphase flow process showed superior performance in missing value imputation and dynamic process monitoring.

    Conclusions:

    • The masked MD-VAE model provides a robust solution for representation learning in industrial processes with missing data.
    • The unified framework enables effective application of extracted LVs for critical industrial operational tasks.
    • This approach significantly improves the reliability and applicability of data-driven methods in challenging industrial environments.