Quality related fault detection based on dynamic-inner convolutional autoencoder and partial least squares and its application to ironmaking process

Wu Ping; Ni Yuxuan; Wang Huaimin; Hu Xuguang; Wu Zhenquan; Jiang Jian; Hu Yaowu

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Quality related fault detection based on dynamic-inner convolutional autoencoder and partial least squares and its application to ironmaking process

Updated：2026-03-13

- Quality related fault detection based on dynamic-inner convolutional autoencoder and partial least squares and its application to ironmaking process
- Chinese Journal of Chemical Engineering Vol. 89, Issue 1, Pages: 267-276(2026)
- Affiliations：
  
  School of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China
  Changshan Research Institute, Zhejiang Sci-Tech University, Changshan 324299, China
  Zhejiang Sanhua Automotive Components Co., Ltd., Hangzhou 310018, China
  Zhejiang Deli Machine Manufacturer Co., Ltd., Lishui 321499, China
  Jinyun Research Institute, Zhejiang Sci-Tech University, Jinyun 321499, China
- Author bio：
  
  Corresponding author. School of Information Science and Engineering, Zhe-jiang Sci-Tech University, Hangzhou, 310018, China. E-mail address: pingwu@zstu.edu.cn(P. Wu).
- Funds：
- DOI：
  CLC：
- Received：20 March 2025，
  
  Revised：2025-08-21，
  
  Accepted：22 August 2025，
  
  Online First：15 October 2025，
  
  Published：2026-01
- Accepted：
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Wu Ping, Ni Yuxuan, Wang Huaimin, et al. Quality related fault detection based on dynamic-inner convolutional autoencoder and partial least squares and its application to ironmaking process[J]. Chinese Journal of Chemical Engineering, 2026, 89(1): 267-276.
DOI：

Wu Ping, Ni Yuxuan, Wang Huaimin, et al. Quality related fault detection based on dynamic-inner convolutional autoencoder and partial least squares and its application to ironmaking process[J]. Chinese Journal of Chemical Engineering, 2026, 89(1): 267-276. DOI：

摘要

Abstract

Partial least squares (PLS) model maximizes the covariance between process variables and quality variables

making it widely used in quality-related fault detection. However

traditional PLS methods focus primarily on linear processes

leading to poor performance in dynamic nonlinear processes. In this paper

a novel quality-related fault detection method

named DiCAE-PLS

is developed by combining dynamic-inner convolutional autoencoder with PLS. In the proposed DiCAE-PLS method

latent features are first extracted through dynamic-inner convolutional autoencoder (DiCAE) to capture process dynamics and nonlinearity from process variables. Then

a PLS model is established to build the relationship between the extracted latent features and the final product quality. To detect quality-related faults

Hotelling's

statistic is employed. The developed quality-related fault detection is applied to the widely used industrial benchmark of the Tennessee.

关键词

Keywords

references

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