Review and perspectives of data-driven distributed monitoring for industrial plant-wide processes
Process monitoring is crucial for maintaining favorable operating conditions and has
received considerable attention in previous decades. Currently, a plant-wide process …
received considerable attention in previous decades. Currently, a plant-wide process …
Parallel PCA–KPCA for nonlinear process monitoring
Q Jiang, X Yan - Control Engineering Practice, 2018 - Elsevier
Both linear and nonlinear relationships may exist among process variables, and monitoring
a process with such complex relationships among variables is imperative. However …
a process with such complex relationships among variables is imperative. However …
Canonical variate dissimilarity analysis for process incipient fault detection
KES Pilario, Y Cao - IEEE Transactions on Industrial …, 2018 - ieeexplore.ieee.org
Early detection of incipient faults in industrial processes is increasingly becoming important,
as these faults can slowly develop into serious abnormal events, an emergency situation, or …
as these faults can slowly develop into serious abnormal events, an emergency situation, or …
Deep learning for fault-relevant feature extraction and fault classification with stacked supervised auto-encoder
Abstract Stacked auto-encoder (SAE)-based deep learning has been introduced for fault
classification in recent years, which has the potential to extract deep abstract features from …
classification in recent years, which has the potential to extract deep abstract features from …
Sensor drift fault diagnosis for chiller system using deep recurrent canonical correlation analysis and k-nearest neighbor classifier
L Gao, D Li, L Yao, Y Gao - ISA transactions, 2022 - Elsevier
Early detection and diagnosis of the chiller sensor drift fault are crucial to maintain normal
operation for energy saving. Due to the complex physical structure and operation conditions …
operation for energy saving. Due to the complex physical structure and operation conditions …
Local–global modeling and distributed computing framework for nonlinear plant-wide process monitoring with industrial big data
Industrial big data and complex process nonlinearity have introduced new challenges in
plant-wide process monitoring. This article proposes a local-global modeling and distributed …
plant-wide process monitoring. This article proposes a local-global modeling and distributed …
Deep quality-related feature extraction for soft sensing modeling: A deep learning approach with hybrid VW-SAE
Soft sensors have been extensively used to predict difficult-to-measure quality variables for
effective modeling, control and optimization of industrial processes. To construct accurate …
effective modeling, control and optimization of industrial processes. To construct accurate …
Data-driven batch-end quality modeling and monitoring based on optimized sparse partial least squares
Batch-end quality modeling is used to predict the quality by using batch measurements and
generally involves a large number of predictor variables. However, not all of the variables …
generally involves a large number of predictor variables. However, not all of the variables …
Deep learning for quality prediction of nonlinear dynamic processes with variable attention‐based long short‐term memory network
Industrial processes are often characterized with high nonlinearities and dynamics. For soft
sensor modelling, it is important to model the nonlinear and dynamic relationship between …
sensor modelling, it is important to model the nonlinear and dynamic relationship between …
LDA-based deep transfer learning for fault diagnosis in industrial chemical processes
Y Wang, D Wu, X Yuan - Computers & Chemical Engineering, 2020 - Elsevier
Deep transfer network (DTN) has been widely used for classification tasks, which introduces
maximum mean discrepancy (MMD) based loss function to extract similar latent features and …
maximum mean discrepancy (MMD) based loss function to extract similar latent features and …