Related references
Note: Only part of the references are listed.Multisubspace Orthogonal Canonical Correlation Analysis for Quality-Related Plant-Wide Process Monitoring
Bing Song et al.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2021)
Enhanced moving horizon Bayesian-based fault diagnosis for multisampling rate data in a plantwide process
Ying Tian et al.
MEASUREMENT (2020)
Distributed fault diagnosis of rotor-bearing system using dimensional analysis and experimental methods
Prakash M. Jadhav et al.
MEASUREMENT (2020)
Incipient Fault Detection for Multiphase Batch Processes With Limited Batches
Shumei Zhang et al.
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY (2019)
Slow-Feature-Analysis-Based Batch Process Monitoring With Comprehensive Interpretation of Operation Condition Deviation and Dynamic Anomaly
Shumei Zhang et al.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2019)
Parallel quality-related dynamic principal component regression method for chemical process monitoring
Yang Tao et al.
JOURNAL OF PROCESS CONTROL (2019)
Model based insulation fault diagnosis for lithium-ion battery pack in electric vehicles
Yujie Wang et al.
MEASUREMENT (2019)
Relevant and independent multi-block approach for plant-wide process and quality-related monitoring based on KPCA and SVDD
Junping Huang et al.
ISA TRANSACTIONS (2018)
Large-scale plant-wide process modeling and hierarchical monitoring: A distributed Bayesian network approach
Jinlin Zhu et al.
JOURNAL OF PROCESS CONTROL (2018)
A novel dynamic PCA algorithm for dynamic data modeling and process monitoring
Yining Dong et al.
JOURNAL OF PROCESS CONTROL (2018)
A feature extraction and visualization method for fault detection of marine diesel engines
Wenkui Xi et al.
MEASUREMENT (2018)
Fault detection of broken rotor bar in LS-PMSM using random forests
Juan C. Quiroz et al.
MEASUREMENT (2018)
Quality-Related Fault Detection and Diagnosis Based on Total Principal Component Regression Model
Guang Wang et al.
IEEE ACCESS (2018)
Distributed Parallel PCA for Modeling and Monitoring of Large-Scale Plant-Wide Processes With Big Data
Jinlin Zhu et al.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2017)
Linear Subspace Principal Component Regression Model for Quality Estimation of Nonlinear and Multimode Industrial Processes
Junhua Zhen et al.
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH (2017)
Concurrent quality and process monitoring with canonical correlation analysis
Qinqin Zhu et al.
JOURNAL OF PROCESS CONTROL (2017)
Online process monitoring for complex systems with dynamic weighted principal component analysis
Zhengshun Fei et al.
CHINESE JOURNAL OF CHEMICAL ENGINEERING (2016)
Hidden Markov Model-Based Fault Detection Approach for a Multimode Process
Fan Wang et al.
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH (2016)
Supervised linear dynamic system model for quality related fault detection in dynamic processes
Zhiqiang Ge et al.
JOURNAL OF PROCESS CONTROL (2016)
Quality-related fault detection using linear and nonlinear principal component regression
Guang Wang et al.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS (2016)
Multiblock Independent Component Analysis Integrated with Hellinger Distance and Bayesian Inference for Non-Gaussian Plant-Wide Process Monitoring
Qingchao Jiang et al.
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH (2015)
A New Method of Dynamic Latent-Variable Modeling for Process Monitoring
Gang Li et al.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2014)
A Review on Basic Data-Driven Approaches for Industrial Process Monitoring
Shen Yin et al.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2014)
Data-driven design of monitoring and diagnosis systems for dynamic processes: A review of subspace technique based schemes and some recent results
S. X. Ding
JOURNAL OF PROCESS CONTROL (2014)
Quality-Relevant and Process-Relevant Fault Monitoring with Concurrent Projection to Latent Structures
S. Joe Qin et al.
AICHE JOURNAL (2013)
Enhanced batch process monitoring using just-in-time-learning based kernel partial least squares
Yi Hu et al.
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS (2013)
A Novel Scheme for Key Performance Indicator Prediction and Diagnosis With Application to an Industrial Hot Strip Mill
Steven X. Ding et al.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2013)
Chemical processes monitoring based on weighted principal component analysis and its application
Qingchao Jiang et al.
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS (2012)
A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process
Shen Yin et al.
JOURNAL OF PROCESS CONTROL (2012)
Quality Relevant Data-Driven Modeling and Monitoring of Multivariate Dynamic Processes: The Dynamic T-PLS Approach
Gang Li et al.
IEEE TRANSACTIONS ON NEURAL NETWORKS (2011)
Local dynamic partial least squares approaches for the modelling of batch processes
N. M. Fletcher et al.
CANADIAN JOURNAL OF CHEMICAL ENGINEERING (2008)
Model-based fault-detection and diagnosis - status and applications
R Isermann
ANNUAL REVIEWS IN CONTROL (2005)
Community structure in social and biological networks
M Girvan et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2002)
The application of principal component analysis and kernel density estimation to enhance process monitoring
Q Chen et al.
CONTROL ENGINEERING PRACTICE (2000)
Share Paper
