Quality‐related process monitoring approach based on sparse autoencoder and comprehensive KPLS

The partial least squares (PLS) model is widely employed in quality‐related process monitoring due to its ability to effectively establish a linear relationship between process and quality variables. To extend this capability to nonlinear scenarios, kernel partial least squares (KPLS) was introduced...

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Bibliographic Details
Published in:Canadian journal of chemical engineering Vol. 103; no. 10; pp. 4939 - 4951
Main Authors: Xue, Yikai, Pan, Haipeng, Wu, Ping, Ye, Zhenyu, Zhou, Haiyun, Wu, Zhenquan
Format: Journal Article
Language:English
Published: Hoboken, USA John Wiley & Sons, Inc 01.10.2025
Wiley Subscription Services, Inc
Subjects:
Fault detection
Feature extraction
Hot rolling
Hot rolling mills
Kernel functions
kernel partial least squares (KPLS)
Least squares
Monitoring
Nonlinearity
process monitoring
Process variables
quality‐related
sparse autoencoder (SAE)
Subspaces
ISSN:0008-4034, 1939-019X
Online Access:Get full text
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Summary:The partial least squares (PLS) model is widely employed in quality‐related process monitoring due to its ability to effectively establish a linear relationship between process and quality variables. To extend this capability to nonlinear scenarios, kernel partial least squares (KPLS) was introduced. However, the use of a single kernel function is often inadequate for fully capturing nonlinearity. In this paper, a novel method for quality‐related process monitoring that integrates sparse autoencoders (SAE) with two KPLS models, termed SAE‐CKPLS, is developed. The SAE is utilized to extract representative features from the process variables, after which two KPLS models are constructed to explore the relationship between these extracted features and residuals with the quality variables. Additionally, two Hotelling's T2 monitoring statistics are derived from the decomposed subspaces to detect quality‐related faults. The capability and effectiveness of the proposed SAE‐CKPLS method are demonstrated through applications to both a hot rolling mill process and the industrial Tennessee Eastman process (TEP) benchmark, with comparative analysis against related methods.
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ISSN:0008-4034
1939-019X
DOI:10.1002/cjce.25690