Autoencoder-Based Dempster-Shafer Decision-Making Framework for Intelligent Process Monitoring: Applications to Nonlinear Chemical Processes
- 1Universiti Sains Malaysia , , Engineering Campus Seri Ampangan , , ,
Abstract
Industrial chemical processes are nonlinear, large-scale, and strongly coupled, making them susceptible to disturbances that threaten operational safety. Conventional model-based and deep learning approaches often struggle with nonlinear dynamics, limited labeled fault data, detection latency, and diagnostic uncertainty. This study presents an intelligent process monitoring framework integrating autoencoder (AE)-based feature learning using denoising autoencoder (DAE), sparse autoencoder (SAE), and variational autoencoder (VAE) models with Dempster–Shafer theory (DST) for uncertainty-aware decision fusion. The framework was evaluated on a benchmark continuous stirred-tank reactor data set containing 12 fault scenarios and normal operating conditions. Baseline accuracies of 87% (DAE), 79% (SAE), and 74% (VAE) improved to 93%, 92%, and 90%, respectively, after DST integration. Following this integration, DAE-DST reduced the false alarm rate (FAR) to 1.41%, increased the G-mean to 0.911, and shortened the mean detection delay (MDD) to 0.56 min. The proposed framework provides a robust and computationally efficient solution for reliable real-time process monitoring in nonlinear chemical processes.
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BibTeX
@article{ewuzie2026autoencoderbased,
title = {{Autoencoder-Based Dempster-Shafer Decision-Making Framework for Intelligent Process Monitoring: Applications to Nonlinear Chemical Processes}},
author = {Remigius Nnadozie Ewuzie and Shivaneswar Gunasekaran and Zainal Ahmad and Norazwan Md Nor},
journal = {ACS Omega},
year = {2026},
month = oct,
doi = {10.1021/acsomega.6c08762},
url = {https://pubs.acs.org/acsodf/article/doi/10.1021/acsomega.6c08762/5456981/Autoencoder-Based-Dempster-Shafer-Decision-Making},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Autoencoder-Based Dempster-Shafer Decision-Making Framework for Intelligent Process Monitoring: Applications to Nonlinear Chemical Processes
AU - Remigius Nnadozie Ewuzie
AU - Shivaneswar Gunasekaran
AU - Zainal Ahmad
AU - Norazwan Md Nor
PY - 2026
DA - 2026/10/23
JO - ACS Omega
DO - 10.1021/acsomega.6c08762
UR - https://pubs.acs.org/acsodf/article/doi/10.1021/acsomega.6c08762/5456981/Autoencoder-Based-Dempster-Shafer-Decision-Making
AB - Industrial chemical processes are nonlinear, large-scale, and strongly coupled, making them susceptible to disturbances that threaten operational safety. Conventional model-based and deep learning approaches often struggle with nonlinear dynamics, limited labeled fault data, detection latency, and diagnostic uncertainty. This study presents an intelligent process monitoring framework integrating autoencoder (AE)-based feature learning using denoising autoencoder (DAE), sparse autoencoder (SAE), and variational autoencoder (VAE) models with Dempster–Shafer theory (DST) for uncertainty-aware decision fusion. The framework was evaluated on a benchmark continuous stirred-tank reactor data set containing 12 fault scenarios and normal operating conditions. Baseline accuracies of 87% (DAE), 79% (SAE), and 74% (VAE) improved to 93%, 92%, and 90%, respectively, after DST integration. Following this integration, DAE-DST reduced the false alarm rate (FAR) to 1.41%, increased the G-mean to 0.911, and shortened the mean detection delay (MDD) to 0.56 min. The proposed framework provides a robust and computationally efficient solution for reliable real-time process monitoring in nonlinear chemical processes.
SN - 2470-1343
ER -