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Multirate Industrial Process Forecasting With Hybrid Deep Learning and Adaptive Filtering.

Xianyao Han, Wen Yu, Yao Jia

    IEEE Transactions on Neural Networks and Learning Systems
    |November 20, 2025
    PubMed
    Summary

    This study introduces a hybrid deep learning framework for accurate industrial process forecasting, significantly improving prediction accuracy and robustness in multirate environments with missing data.

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    Area of Science:

    • Industrial Process Control
    • Machine Learning
    • Data Science

    Background:

    • Multirate industrial processes present forecasting challenges due to variable sampling rates and missing data.
    • Accurate forecasting is crucial for optimizing industrial operations and ensuring product quality.

    Purpose of the Study:

    • To develop a novel hybrid deep learning framework for enhanced forecasting in multirate industrial processes.
    • To address the challenges of varying sampling frequencies and missing quality variables.

    Main Methods:

    • A hybrid deep learning framework combining time series decomposition, inverted transformer (iTransformer) for feature extraction, and a modified minimal gated unit (MGU) network.
    • Implementation of a robust adaptive parameter update algorithm using dead-zone Kalman filtering to handle missing data.

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  • Validation on real-world industrial datasets.

    • Main Results:

    • Achieved a 61.42% reduction in Mean Absolute Error (MAE).
    • Demonstrated a 64.11% reduction in Root-Mean-Square Error (RMSE).
    • Improved qualification rate by 14.73% compared to existing methods.

    Conclusions:

    • The proposed hybrid deep learning framework significantly enhances forecasting accuracy and robustness in multirate industrial processes.
    • The novel approach effectively handles missing data and outperforms current state-of-the-art techniques.
    • This method offers a promising solution for improving industrial process monitoring and control.