Deep Learning Framework for Collaborative Variable Time Delay Estimation and Uncertainty Quantification in Industrial Quality Prediction

doi:10.1109/TII.2024.3495788

Deep Learning Framework for Collaborative Variable Time Delay Estimation and Uncertainty Quantification in Industrial Quality Prediction

2025-02-01

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DOI：10.1109/TII.2024.3495788

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Deep learning offers promising solutions for quality prediction in industrial processes, improving decision-making and performance monitoring. In this article, we propose a novel deep learning framework that incorporates variable time delay (VTD) estimation and uncertainty quantification into quality prediction. The framework employs a collaborative method that integrates deep Q-network with random forest to estimate VTD values. It then utilizes a hybrid BMCR model, consisting of parallel bidirectional minimal gated unit and 1-D convolutional layers, along with a residual connection, specifically designed to capture both long-term and short-term features in industrial data. The framework produces prediction intervals directly to quantify the uncertainty in the prediction results. This combined method offers high-precision point predictions alongside uncertainty quantification, providing valuable insights for industrial decision-making. The effectiveness of the proposed method is validated through two numerical examples, a benchmark, and a real-world industrial case from the alumina digestion process.

Keywords:

Estimation

Uncertainty

Radio frequency

Collaboration

Deep learning

Delay effects

Delays

Accuracy

Predictive models

Logic gates

Deep Q-network (DQN)

prediction intervals (PIs)

quality prediction

variable time delay (VTD)

uncertainty quantification

Deep Learning Framework for Collaborative Variable Time Delay Estimation and Uncertainty Quantification in Industrial Quality Prediction

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