S. Joe Qin

发表

Map-Reduce Decentralized PCA for Big Data Monitoring and Diagnosis of Faults in High-Speed Train Bearings

Qiang Liu, Dezhi Kong, S. Joe Qin, 2018 .

Joint diagnosis of process and sensor faults using principal component analysis

S. Joe Qin, Ricardo Dunia, S. Qin, 1998 .

Regularized LTI System Identification with Multiple Regularization Matrix

Lennart Ljung, S. Joe Qin, Tianshi Chen, 2018 .

Recursive PCA for Adaptive Process Monitoring

Weihua Li, S. Joe Qin, H. Henry Yue, 1999 .

Fisher Discriminant Analysis for Semiconductor Batch Tool Matching

S. Joe Qin, Gregory A. Cherry, 2008 .

Dynamic-Inner Partial Least Squares for Dynamic Data Modeling

S. Joe Qin, Yining Dong, S. Qin, 2015 .

Use of principal component analysis for sensor fault identification

Thomas F. Edgar, S. Joe Qin, Thomas J. McAvoy, 1996 .

Statistical process monitoring: basics and beyond

S. Joe Qin, S. Qin, 2003 .

Model Predictive Control of FCC Units with Integrated Sensor Validation and Process Fault Diagnosis

S. Joe Qin, Toto Nugroho Pranatyasto, 2000 .

Adaptive actuator fault compensation for linear systems with matching and unmatching uncertainties

Yingwei Zhang, S. Joe Qin, Ying-wei Zhang, 2009 .

Control performance monitoring — a review and assessment

S. Joe Qin, S. Qin, 1998 .

Projection based MIMO control performance monitoring: II––measured disturbances and setpoint changes

S. Joe Qin, Christopher A. McNabb, S. Qin, 2005 .

Closed-Loop Time Delay Estimation of SISO Processes for Control Performance Monitoring

S. Joe Qin, Christopher A. Harrison, 2004 .

Sensor Fault Identification and Reconstruction Using Principal Component Analysis

Thomas F. Edgar, S. Joe Qin, Thomas J. McAvoy, 1996 .

Multiscale Kernel Based Residual Convolutional Neural Network for Motor Fault Diagnosis Under Nonstationary Conditions

Fei Wang, S. Joe Qin, Boyuan Yang, 2020, IEEE Transactions on Industrial Informatics.

Recursive PCA for adaptive process monitoring

Weihua Li, S. Joe Qin, H. Henry Yue, 1999 .

Computer simulation of gas generation and transport in landfills: VI—Dynamic updating of the model using the ensemble Kalman filter

Muhammad Sahimi, S. Joe Qin, Hu Li, 2012 .

Reconstruction based fault prognosis for continuous processe

Donghua Zhou, S. Joe Qin, Gang Li, 2009 .

Drill-down diagnosis of deficient models in MPC

S. Joe Qin, Lijuan Li, 2015 .

A two-stage algorithm for combined iterative learning control with real-time feedback; a state space

S. Joe Qin, Kwang S. Lee, Moonki Cho, 2005 .

Reconstruction-based Contribution for Process Monitoring

S. Joe Qin, Carlos F. Alcala, 2008 .

Closed-Loop Subspace Identification with Innovation Estimation

Lennart Ljung, S. Joe Qin, L. Ljung, 2003 .

Semiconductor manufacturing process control and monitoring: A fab-wide framework

S. Joe Qin, Jin Wang, Richard Good, 2006 .

Data-driven Fault Detection and Diagnosis for Complex Industrial Processes

S. Joe Qin, S. Qin, S. Qin, 2009 .

Sensor validation and process fault diagnosis for FCC units under MPC feedback

S. Joe Qin, Toto Nugroho Pranatyasto, 2001 .

Fault Diagnosis Using Concurrent Projection to Latent Structures

S. Joe Qin, Yining Dong, Yu C. Pan, 2015 .

Fault Diagnosis of Continuous Annealing Processes Using Reconstruction-based Block Contribution

Tianyou Chai, Qiang Liu, S. Joe Qin, 2011 .

Actuator Fault Compensation Control for Nonlinear Systems

Yingwei Zhang, S. Joe Qin, 2008 .

A model Reduction Method for Monitoring Large-scale Processes

Uwe Kruger, S. Joe Qin, Xun Wang, 2001 .

Fault diagnosis of continuous annealing processes using a reconstruction-based method

Tianyou Chai, Qiang Liu, S. Joe Qin, 2012 .

Decentralized Fault Diagnosis of Large-scale Processes Using Multiblock Kernel Principal Component Analysis: Decentralized Fault Diagnosis of Large-scale Processes Using Multiblock Kernel Principal Component Analysis

S. Joe Qin, Hong Zhou, Ying-Wei Zhang, 2010 .

Multivariate process monitoring and fault diagnosis by multi-scale PCA

S. Joe Qin, Cheng Ling, H. Henry Yue, 2002 .

Control and Monitoring of Semiconductor Manufacturing Processes: Challenges and Opportunities

Jin Wang, S. Joe Qin, Richard Good, 2004 .

Computationally efficient modeling of wafer temperatures in an LPCVD furnace

Qinghua He, S. Joe Qin, Anthony J. Toprac, 2003, SPIE Advanced Lithography.

Subspace approach to multidimensional fault identification and reconstruction

S. Joe Qin, Ricardo Dunia, S. Qin, 1998 .

Sensor Fault Compensation for Nonlinear Systems Using Fuzzy Adaptive Sliding Control

Yingwei Zhang, S. Joe Qin, S. Qin, 2008 .

Dynamic Processes Modeling and Monitoring based on a Novel Dynamic Latent Variable Model

Zhiqiang Ge, S. Joe Qin, Zhihuan Song, 2019, 2019 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS).

An Overview of Nonlinear Model Predictive Control Applications

S. Joe Qin, Thomas A. Badgwell, T. A. Badgwell, 2000 .

Parallel QR Implementation of Subspace Identification with Parsimonious Models

Lennart Ljung, S. Joe Qin, 2003 .

A TWO-STAGE ALGORITHM FOR COMBINED ITERATIVE LEARNING CONTROL WITH REAL-TIME FEEDBACK; A STATE SPACE FORMULATION

S. Joe Qin, Moonki Cho, Kwang S. Lee, 2005 .

COMPARISONS OF SUBSPACE IDENTIFICATION METHODS FOR SYSTEMS OPERATING ON CLOSED-LOOP

Lennart Ljung, S. Joe Qin, Weilu Lin, 2005 .

Ying Zheng, S. Joe Qin, Fu-li Wang, 2015 .

On unifying multiblock analysis with application to decentralized process monitoring

Michael J. Piovoso, S. Joe Qin, Sergio Valle, 2001 .

Subspace model identification under load disturbance with unknown transient and periodic dynamics

S. Joe Qin, Wei Wang, Tao Liu, 2020 .

Reconstruction-Based Fault Identification Using a Combined Index

S. Joe Qin, H. Henry Yue, S. Qin, 2001 .

A survey of industrial model predictive control technology

S. Joe Qin, Thomas A. Badgwell, T. A. Badgwell, 2003 .

Offline Predictive Control of Out-of-Plane Shape Deformation for Additive Manufacturing

Qiang Huang, Yuan Jin, S. Joe Qin, 2016 .

Dynamic latent variable regression for inferential sensor modeling and monitoring

S. Joe Qin, Qinqin Zhu, Yining Dong, 2020, Comput. Chem. Eng..

Distributed Approach for Temporal–Spatial Charging Coordination of Plug-in Electric Taxi Fleet

S. Joe Qin, Pengcheng You, Yunhe Hou, 2019, IEEE Transactions on Industrial Informatics.

Fault Detection of Multimode Processes Using Concurrent Projection to Latent Structures

Tianyou Chai, S. Joe Qin, Ying Zheng, 2016 .

Sliding window games for cooperative building temperature control using a distributed learning method

Tao Yuan, Zhaohui Zhang, Ruilong Deng, 2017 .

A Subspace Approach to MIMO Control Performance Monitoring and Diagnosis

S. Joe Qin, Christopher A. McNabb, 2004 .

Special issue on process dynamics and control

Jay H. Lee, S. Joe Qin, En-Sup Yoon, 2003 .

Control Performance Monitoring of Excessive Oscillations of an Offshore Production Facility

S. Joe Qin, Yingying Zheng, Michael Dean Barham, 2012 .

Curve Fitting for Detecting Valve Stiction

S. Joe Qin, Q. Peter He, 2010 .

Analysis and generalization of fault diagnosis methods for process monitoring

S. Joe Qin, Carlos F. Alcala, S. Qin, 2011 .

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

S. Joe Qin, Yingying Zheng, S. Qin, 2013 .

Performance monitoring of model-predictive controllers via model residual assessment

S. Joe Qin, Larry Megan, Ashish Singhal, 2013 .

An Alternative Stiction-modelling Approach and Comparison of Different Stiction Models

Jin Wang, S. Joe Qin, Q. Peter He, 2010 .

Monitoring of Dynamic Processes with Subspace Identification and Principal Component Analysis

S. Joe Qin, Ricardo Dunia, Carlos F. Alcala, 2012 .

Regression on dynamic PLS structures for supervised learning of dynamic data

S. Joe Qin, Yining Dong, S. Qin, 2018, Journal of Process Control.

Fault Detection and Operation Mode Identification Based on Pattern Classification with Variable Selection

Chonghun Han, S. Joe Qin, Young-Hwan Chu, 2004 .

Bridging systems theory and data science: A unifying review of dynamic latent variable analytics and process monitoring

Qiang Liu, S. Joe Qin, Jin Wang, 2020, Annu. Rev. Control..

ELECTRICAL PARAMETER CONTROL FOR SEMICONDUCTOR DEVICE MANUFACTURING: A FABWIDE APPROACH

S. Joe Qin, Erhan Kutanoglu, John Stuber, 2007 .

ON THE ROLE OF FUTURE HORIZON IN CLOSED-LOOP SUBSPACE IDENTIFICATION

Lennart Ljung, S. Joe Qin, 2006 .

MULTIVARIABLE CONTROLLER PERFORMANCE MONITORING

S. Joe Qin, Jie Yu, 2006 .

Unified Analysis of Diagnosis Methods for Process Monitoring

S. Joe Qin, Carlos F. Alcala, 2009 .

Book review: Subspace methods for system identification

S. Joe Qin, S. Qin, 2007 .

KPCA based multi-spectral segments feature extraction and GA based Combinatorial optimization for frequency spectrum data modeling

Jian Tang, S. Joe Qin, Tianyou Chai, 2011, IEEE Conference on Decision and Control and European Control Conference.

Total projection to latent structures for process monitoring

Donghua Zhou, Gang Li, S. Joe Qin, 2009 .

Dynamic latent variable modeling for statistical process monitoring

Donghua Zhou, S. Joe Qin, Gang Li, 2011 .

A Platform for Fault Diagnosis of High-Speed Train based on Big Data

Zhang Peng, Qiang Liu, Changxin Liu, 2018 .

Statistical MIMO controller performance monitoring. Part I: Data-driven covariance benchmark

S. Joe Qin, Jie Yu, S. Qin, 2008 .

On the Convergence of the Dynamic Inner PCA Algorithm pdf

Victor M. Zavala, Sungho Shin, S. Joe Qin, 2020, ArXiv.

Identification of faulty sensors using principal component analysis

Thomas F. Edgar, S. Joe Qin, Thomas J. McAvoy, 1996 .

Fault Detection of Non-Linear Processes Using Kernel Independent Component Analysis

In-Beum Lee, S. Joe Qin, Jong-Min Lee, 2008 .

Maximizing Fault Detectability with Closed-Loop Control

S. Joe Qin, Zhijie Sun, 2018 .

Statistical MIMO controller performance monitoring. Part II: Performance diagnosis

S. Joe Qin, Jie Yu, S. Qin, 2008 .

Fault Detection for MIMO Networked Control System

Fuli Wang, Yingwei Zhang, S. Joe Qin, 2006 .

Adaptive Actuator/Component Fault Compensation for Nonlinear Systems

Yingwei Zhang, S. Joe Qin, 2008 .

Discriminating between disturbance and process model mismatch in model predictive control

S. Joe Qin, Christopher A. Harrison, S. Qin, 2009 .

Process Variability Source Analysis for a Multi-step Bio-process

Zheng Li, S. Joe Qin, Salim Charaniya, 2018 .

Dynamic-Inner Canonical Correlation Analysis based Process Monitoring

S. Joe Qin, Yining Dong, Yining Dong, 2020, 2020 American Control Conference (ACC).

Variance component analysis based fault diagnosis of multi-layer overlay lithography processes

S. Joe Qin, Jie Yu, 2009 .

Fault detection and diagnosis based on modified independent component analysis

In-Beum Lee, S. Joe Qin, Jong-Min Lee, 2006 .

Nonstationarity and cointegration tests for fault detection of dynamic processes

Tao Yuan, S. Joe Qin, Gang Li, 2014 .

Adaptive subspace system identification for CO 2 capture processes

S. Joe Qin, Gary T. Rochelle, Ricardo Dunia, 2011 .

Detection and Diagnosis of Stiction in Control Loops State of the Art and Advanced Methods

Nina F. Thornhill, Manabu Kano, S. Joe Qin, 2010 .

A unified geometric approach to process and sensor fault identification and reconstruction : The unidimensional fault case

S. Joe Qin, Ricardo Dunia, S. Qin, 1998 .

Recent developments in multivariable controller performance monitoring

S. Joe Qin, Jie Yu, S. Qin, 2007 .

Achieving state estimation equivalence for misassigned disturbances in offset‐free model predictive control

James B. Rawlings, S. Joe Qin, Murali R. Rajamani, 2009 .

Fault diagnosis and isolation of multi-input-multi-output networked control systems

Jie Sheng, Yingwei Zhang, S. Joe Qin, 2008 .

Detection, identification, and reconstruction of faulty sensors with maximized sensitivity

Weihua Li, S. Joe Qin, S. Qin, 1999 .

Hybrid Latent Variable Modeling of High Dimensional Time Series Data

S. Joe Qin, Yining Dong, 2018 .

Reconstruction based fault prognosis for continuous processes

Gang Li, Donghua Zhou, S. Joe Qin, 2010 .

Modified Independent Component Analysis for Multivariate Statistical Process Monitoring

In-Beum Lee, S. Joe Qin, Jong-Min Lee, 2006 .

Classification and Diagnosis of Bioprocess Cell Growth Productions Using Early-Stage Data

Zheng Li, S. Joe Qin, Salim Charaniya, 2019, Industrial & Engineering Chemistry Research.

Ensembles‐based and GA‐based optimization for landfill gas production

Muhammad Sahimi, S. Joe Qin, Hu Li, 2014 .

Extracting a low-dimensional predictable time series

Stephen P. Boyd, S. Joe Qin, Stephen Boyd, 2021, Optimization and Engineering.

Consistent dynamic PCA based on errors-in-variables subspace identification

S. Joe Qin, S. Qin, Weihua Li, 2001 .

MIMO control performance monitoring using left/right diagonal interactors

S. Joe Qin, Jie Yu, S. Qin, 2009 .

CONTROL OF GASHOLDER LEVEL BY TREND PREDICTION BASED ON TIME-SERIES ANALYSIS AND PROCESS HEURISTICS

Chonghun Han, S. Joe Qin, Young-Hwan Chu, 2003 .

S. Joe Qin, Ying-Wei Zhang, S. Qin, 2010 .

An interpolating model predictive control strategy with application to a waste treatment plant

Bjarne A. Foss, S. Joe Qin, Víctor M. Martínez, 1997 .

A NEW SUBSPACE IDENTIFICATION METHOD FOR CLOSED-LOOP SYSTEMS

S. Joe Qin, Genichi Emoto, Koichi Onodera, 2006 .

Computer simulation of gas generation and transport in landfills. V: Use of artificial neural network and the genetic algorithm for short- and long-term forecasting and planning

Muhammad Sahimi, S. Joe Qin, Raudel Sanchez, 2011 .

Adaptive run-to-run control and monitoring for a rapid thermal processor

S. Joe Qin, Glen W. Scheid, Terrence J. Riley, 2003 .

Comparative study on monitoring schemes for non-Gaussian distributed processes

S. Joe Qin, S. Qin, Gang Li, 2016, Journal of Process Control.

Dynamic time warping based causality analysis for root-cause diagnosis of nonstationary fault processes

Tianyou Chai, S. Joe Qin, T. Chai, 2015 .

Performance Synthesis of Multiple Input−Multiple Output (MIMO) Exponentially Weighted Moving Average (EWMA) Run-to-Run Controllers with Metrology Delay

S. Joe Qin, S. Qin, R. Good, 2011 .

Dynamic Nonlinear Partial Least Squares Modeling Using Gaussian Process Regression

Bengt Carlsson, ChangKyoo Yoo, S. Joe Qin, 2019, Industrial & Engineering Chemistry Research.

Subspace Identification Using the Parity Space

S. Joe Qin, S. Qin, Jin Wang, 2004 .

Optimal Structured Residual Approach for Improved Faulty Sensor Diagnosis

S. Joe Qin, Weilu Lin, 2005 .

Fault Detection of Nonlinear Processes Using Multiway Kernel Independent Component Analysis

S. Joe Qin, Yingwei Zhang, S. Qin, 2007 .

Root cause diagnosis of plant-wide oscillations using Granger causality

S. Joe Qin, S. Qin, Tao Yuan, 2014 .

A Novel Subspace Identiflcation Approach with Parsimonious Parametrization

Lennart Ljung, S. Joe Qin, Weilu Lin, 2004 .

On‐line data compression and error analysis using wavelet technology

S. Joe Qin, Manish Misra, Shailesh Kumar, 2000 .

Feedback-Invariant Approach to Time-Delay Estimation for Performance Monitoring

S. Joe Qin, Christopher A. Harrison, 2012 .

Process data analytics in the era of big data

S. Joe Qin, S. Joe Qin, S. Qin, 2014 .

Interpolating optimizing process control

Bjarne A. Foss, S. Joe Qin, 1997 .

Determining the number of principal components for best reconstruction

S. Joe Qin, Ricardo Dunia, S. Qin, 1998 .

Error based criterion for on-line wavelet data compression

S. Joe Qin, Manish Misra, Shailesh Kumar, 2001 .

Bias-eliminated subspace model identification under time-varying deterministic type load disturbance

Biao Huang, S. Joe Qin, Tao Liu, 2015 .

Latent Variable Regression for Process and Quality Modeling

S. Joe Qin, Qinqin Zhu, 2019, 2019 1st International Conference on Industrial Artificial Intelligence (IAI).

Bi-level Demand Response Game with Information Sharing among Consumers

Ruilong Deng, S. Joe Qin, Zhaohui Zhang, 2016 .

Dynamic-Inner Canonical Correlation and Causality Analysis for High Dimensional Time Series Data

S. Joe Qin, Yining Dong, S. Qin, 2018 .