Adaptive LII-RMPLS based data-driven process monitoring scheme for quality-relevant fault detection

Robust Slow Feature Analysis for Statistical Process Monitoring

(2020) Jiafeng Wang et al.   INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

Fault detection of uncertain chemical processes using interval partial least squares-based generalized likelihood ratio test

(2019) Harkat M.-F. et al.   INFORMATION SCIENCES

Dynamic latent variable analytics for process operations and control

(2018) Yining Dong et al.   COMPUTERS & CHEMICAL ENGINEERING

Recursive Slow Feature Analysis for Adaptive Monitoring of Industrial Processes

(2018) Chao Shang et al.   IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS

Regression on dynamic PLS structures for supervised learning of dynamic data

(2018) Yining Dong et al.   JOURNAL OF PROCESS CONTROL

A Kernel Direct Decomposition-Based Monitoring Approach for Nonlinear Quality-Related Fault Detection

(2017) Guang Wang et al.   IEEE Transactions on Industrial Informatics

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

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

Improved PLS Focused on Key-Performance-Indicator-Related Fault Diagnosis

(2015) Shen Yin et al.   IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS

Adaptive total PLS based quality-relevant process monitoring with application to the Tennessee Eastman process

(2015) Jie Dong et al.   NEUROCOMPUTING

Principal component analysis for non-stationary time series based on detrended cross-correlation analysis

(2015) Xiaojun Zhao et al.   NONLINEAR DYNAMICS

Review of Recent Research on Data-Based Process Monitoring

(2013) Zhiqiang Ge et al.   INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process

(2012) Shen Yin et al.   JOURNAL OF PROCESS CONTROL

Quality Relevant Data-Driven Modeling and Monitoring of Multivariate Dynamic Processes: The Dynamic T-PLS Approach

(2011) Gang Li et al.   IEEE TRANSACTIONS ON NEURAL NETWORKS