A Novel Deep Learning-Based Robust Dual-Rate Dynamic Data Modeling for Quality Prediction

Xiangan Meng, Qiang Liu*, Chao Yang, Le Zhou, Yiu-Ming Cheung

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

Abstract

Traditional data-driven quality prediction methods are mainly built from static models using clean data with a slow sampling rate, leaving the process dynamics unused. To make full use of dynamic process data collected at a fast sampling rate, this article proposes a novel deep learning-based robust dual-rate dynamic data modeling method for quality prediction of dynamic nonlinear processes. A new dynamic data denoising generative adversarial imputation network is first proposed for the missing value imputation among the dynamic process data. Then, a new hint convolutional neural network (HCNN) is established for dual-rate data based quality prediction. The proposed HCNN incorporates the information hint mechanism of channel expansion into the convolutional neural network to extract the dynamic features with definitive time and variable information. Finally, the proposed method is verified using the Dow distillation process dataset and Beijing multisite air quality dataset.

Original languageEnglish
Pages (from-to)1324-1334
Number of pages11
JournalIEEE Transactions on Industrial Informatics
Volume20
Issue number2
Early online date12 May 2023
DOIs
Publication statusPublished - Feb 2024

Scopus Subject Areas

  • Information Systems
  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Computer Science Applications

User-Defined Keywords

  • Data-driven quality prediction
  • dual-rate data modeling
  • dynamic data denoising generative adversarial imputation network (DDGAIN)
  • dynamic process

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