Robust modeling of mixture probabilistic principal component analysis and process monitoring application

Fault detection and diagnosis for data incomplete industrial systems with new Bayesian network approach

(2014) Zhengdao Zhang et al.   Journal of Systems Engineering and Electronics

Fault Detection and Diagnosis in Chemical Processes Using Sensitive Principal Component Analysis

(2013) Qingchao Jiang et al.   INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

Review of Recent Research on Data-Based Process Monitoring

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

Survey on data-driven industrial process monitoring and diagnosis

(2012) S. Joe Qin   ANNUAL REVIEWS IN CONTROL

Chemical processes monitoring based on weighted principal component analysis and its application

(2012) Qingchao Jiang et al.   CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS

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

A particle filter driven dynamic Gaussian mixture model approach for complex process monitoring and fault diagnosis

(2012) Jie Yu   JOURNAL OF PROCESS CONTROL

Mixture Bayesian regularization method of PPCA for multimode process monitoring

(2010) Zhiqiang Ge et al.   AICHE JOURNAL

Maximum-likelihood mixture factor analysis model and its application for process monitoring

(2010) Zhiqiang Ge et al.   CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS

Mixture probabilistic PCR model for soft sensing of multimode processes

(2010) Zhiqiang Ge et al.   CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS

Kernel Generalization of PPCA for Nonlinear Probabilistic Monitoring

(2010) Zhiqiang Ge et al.   INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

Improved kernel PCA-based monitoring approach for nonlinear processes

(2009) Zhiqiang Ge et al.   CHEMICAL ENGINEERING SCIENCE

Robust probabilistic PCA with missing data and contribution analysis for outlier detection

(2009) Tao Chen et al.   COMPUTATIONAL STATISTICS & DATA ANALYSIS

Data-driven Soft Sensors in the process industry

(2009) Petr Kadlec et al.   COMPUTERS & CHEMICAL ENGINEERING

A novel process monitoring approach with dynamic independent component analysis

(2009) Chun-Chin Hsu et al.   CONTROL ENGINEERING PRACTICE

Multimode process monitoring with Bayesian inference-based finite Gaussian mixture models

(2008) Jie Yu et al.   AICHE JOURNAL

Treatment of missing values in process data analysis

(2008) S. A. Imtiaz et al.   CANADIAN JOURNAL OF CHEMICAL ENGINEERING

Probabilistic contribution analysis for statistical process monitoring: A missing variable approach

(2008) Tao Chen et al.   CONTROL ENGINEERING PRACTICE

Dealing with Irregular Data in Soft Sensors: Bayesian Method and Comparative Study

(2008) Shima Khatibisepehr et al.   INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

Mixtures of robust probabilistic principal component analyzers

(2008) Cédric Archambeau et al.   NEUROCOMPUTING

Data-based process monitoring, process control, and quality improvement: Recent developments and applications in steel industry

(2007) Manabu Kano et al.   COMPUTERS & CHEMICAL ENGINEERING