Supervised and semi-supervised probabilistic learning with deep neural networks for concurrent process-quality monitoring

Fault detection and diagnosis based on transfer learning for multimode chemical processes

(2020) Hao Wu et al.   COMPUTERS & CHEMICAL ENGINEERING

Deep learning fault diagnosis method based on global optimization GAN for unbalanced data

(2019) Funa Zhou et al.   KNOWLEDGE-BASED SYSTEMS

Process monitoring using variational autoencoder for high-dimensional nonlinear processes

(2019) Seulki Lee et al.   ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE

Global-and-local-structure-based neural network for fault detection

(2019) Haitao Zhao et al.   NEURAL NETWORKS

Semisupervised Bayesian Gaussian Mixture Models for Non-Gaussian Soft Sensor

(2019) Weiming Shao et al.   IEEE Transactions on Cybernetics

Deep Learning-Based Feature Representation and Its Application for Soft Sensor Modeling With Variable-Wise Weighted SAE

(2018) Xiaofeng Yuan et al.   IEEE Transactions on Industrial Informatics

A Quality-Related Statistical Process Monitoring Method Based on Global plus Local Projection to Latent Structures

(2018) J. L. Zhou et al.   INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

Fault diagnosis for processes with feedback control loops by shifted output sampling approach

(2018) Kai Wang et al.   JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS

Fault Diagnosis of Tennessee-Eastman Process Using Orthogonal Incremental Extreme Learning Machine Based on Driving Amount

(2018) Weidong Zou et al.   IEEE Transactions on Cybernetics

Parallel PCA–KPCA for nonlinear process monitoring

(2018) Qingchao Jiang et al.   CONTROL ENGINEERING PRACTICE

Process Data Analytics via Probabilistic Latent Variable Models: A Tutorial Review

(2018) Zhiqiang Ge   INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

A Data-Driven Soft Sensor Modeling Method Based on Deep Learning and its Application

(2017) Weiwu Yan et al.   IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS

A New Deep Transfer Learning Based on Sparse Auto-Encoder for Fault Diagnosis

(2017) Long Wen et al.   IEEE Transactions on Systems Man Cybernetics-Systems

Data-Driven Process Monitoring Based on Modified Orthogonal Projections to Latent Structures

(2016) Shen Yin et al.   IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY

Quality-Related Statistical Process Monitoring Method Based on Global and Local Partial Least-Squares Projection

(2016) Bin Zhong et al.   INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

Supervised linear dynamic system model for quality related fault detection in dynamic processes

(2016) Zhiqiang Ge et al.   JOURNAL OF PROCESS CONTROL

Semi-supervised PLVR models for process monitoring with unequal sample sizes of process variables and quality variables

(2015) Le Zhou et al.   JOURNAL OF PROCESS CONTROL

Probabilistic latent variable regression model for process-quality monitoring

(2014) Le Zhou et al.   CHEMICAL ENGINEERING SCIENCE

Quality-Related Fault Detection in Industrial Multimode Dynamic Processes

(2014) Adel Haghani et al.   IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS

Quality-Related Process Monitoring Based on Total Kernel PLS Model and Its Industrial Application

(2013) Kaixiang Peng et al.   MATHEMATICAL PROBLEMS IN ENGINEERING

Quality-relevant and process-relevant fault monitoring with concurrent projection to latent structures

(2012) S. Joe Qin et al.   AICHE JOURNAL

Survey on data-driven industrial process monitoring and diagnosis

(2012) S. Joe Qin   ANNUAL REVIEWS IN CONTROL