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Modeling and Monitoring Between-Mode Transition of Multimodes Processes

https://doi.org/10.1109/tii.2012.2220977
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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/aic.11617openalex-snapshot
Improved nonlinear fault detection technique and statistical analysis
inferredcites →https://doi.org/10.1002/aic.12333openalex-snapshot
Spectra data analysis and calibration modeling method using spectra subspace separation and multiblock independent component regression strategy
inferredcites →https://doi.org/10.1002/cem.1011openalex-snapshot
Modelling of multi‐block data
inferredcites →https://doi.org/10.1002/cem.1151openalex-snapshot
Multi‐phase analysis framework for handling batch process data
inferredcites →https://doi.org/10.1002/cem.800openalex-snapshot
Statistical process monitoring: basics and beyond
inferredcites →https://doi.org/10.1002/cem.917openalex-snapshot
PLS regression, PLS path modeling and generalized Procrustean analysis: a combined approach for multiblock analysis
inferredcites →https://doi.org/10.1016/j.aca.2007.02.019openalex-snapshot
Multivariate statistical analysis of a multi-step industrial processes
inferredcites →https://doi.org/10.1016/j.automatica.2009.02.027openalex-snapshot
Reconstruction-based contribution for process monitoring
inferredcites →https://doi.org/10.1016/j.ces.2004.08.007openalex-snapshot
Fault identification for process monitoring using kernel principal component analysis
inferredcites →https://doi.org/10.1016/j.ces.2008.01.022openalex-snapshot
Nonlinear multiscale modelling for fault detection and identification
inferredcites →https://doi.org/10.1016/j.ces.2008.10.012openalex-snapshot
Enhanced statistical analysis of nonlinear processes using KPCA, KICA and SVM
inferredcites →https://doi.org/10.1016/j.ces.2009.01.050openalex-snapshot
Improved kernel PCA-based monitoring approach for nonlinear processes
inferredcites →https://doi.org/10.1016/j.ces.2010.05.010openalex-snapshot
Fault detection of non-Gaussian processes based on modified independent component analysis
inferredcites →https://doi.org/10.1016/j.ces.2010.10.008openalex-snapshot
Fault diagnosis of nonlinear processes using multiscale KPCA and multiscale KPLS
inferredcites →https://doi.org/10.1016/j.ces.2011.06.023openalex-snapshot
Fault magnitude estimation for processes
inferredcites →https://doi.org/10.1016/j.chemolab.2005.11.003openalex-snapshot
Multi-phase principal component analysis for batch processes modelling
inferredcites →https://doi.org/10.1016/j.chemolab.2011.10.013openalex-snapshot
Process monitoring based on mode identification for multi-mode process with transitions
inferredcites →https://doi.org/10.1109/tcst.2006.890282openalex-snapshot
High-Gain Estimator and Fault-Tolerant Design With Application to a Gas Turbine Dynamic System
inferredcites →https://doi.org/10.1109/tcst.2010.2071415openalex-snapshot
Generalized Reconstruction-Based Contributions for Output-Relevant Fault Diagnosis With Application to the Tennessee Eastman Process
inferredcites →https://doi.org/10.1109/tii.2008.2007802openalex-snapshot
Novel Parameter Identification by Using a High-Gain Observer With Application to a Gas Turbine Engine
inferredcites →https://doi.org/10.1109/tii.2009.2020566openalex-snapshot
Kalman Predictive Redundancy System for Fault Tolerance of Safety-Critical Systems
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Nonlinear Dynamic Process Monitoring Using Canonical Variate Analysis and Kernel Density Estimations
inferredcites →https://doi.org/10.1109/tii.2009.2033181openalex-snapshot
Decentralized Fault Diagnosis of Large-Scale Processes Using Multiblock Kernel Partial Least Squares
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An EWMA Algorithm With a Cycled Resetting (CR) Discount Factor for Drift and Fault of High-Mix Run-to-Run Control
inferredcites →https://doi.org/10.1109/tii.2010.2044580openalex-snapshot
Improving Fault Tolerance in High-Precision Clock Synchronization
inferredcites →https://doi.org/10.1109/tnnls.2011.2179669openalex-snapshot
Modeling and Monitoring of Dynamic Processes

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4659527openalex-extracted
Unsupervised Domain Adversarial Network for Few-Sample Fault Detection in Industrial Processes
inferred← citeshttps://doi.org/10.1002/cjce.23163openalex-snapshot
Multimode processes monitoring based on hierarchical mode division and subspace decomposition
inferred← citeshttps://doi.org/10.1007/978-981-10-7212-3_19openalex-snapshot
Multimode Processes Monitoring Using Global–Local MIC-PCA-SVDD
inferred← citeshttps://doi.org/10.1016/j.aei.2024.102684openalex-snapshot
Unsupervised domain adversarial network for few-sample fault detection in industrial processes
inferred← citeshttps://doi.org/10.1016/j.chemolab.2015.10.019openalex-snapshot
Fault detection and classification for complex processes using semi-supervised learning algorithm
inferred← citeshttps://doi.org/10.1016/j.cherd.2015.03.008openalex-snapshot
Comprehensive economic index prediction based operating optimality assessment and nonoptimal cause identification for multimode processes
inferred← citeshttps://doi.org/10.1016/j.cherd.2016.07.014openalex-snapshot
Detecting faults in process systems with singular spectrum analysis
inferred← citeshttps://doi.org/10.1016/j.cjche.2020.09.067openalex-snapshot
Quality oriented multimode processes monitoring based on a novel hierarchical common and specific structure with different order information
inferred← citeshttps://doi.org/10.1016/j.compchemeng.2017.05.029openalex-snapshot
Multi-mode operation of principal component analysis with k-nearest neighbor algorithm to monitor compressors for liquefied natural gas mixed refrigerant processes
inferred← citeshttps://doi.org/10.1016/j.conengprac.2017.02.002openalex-snapshot
Moving window adaptive soft sensor for state shifting process based on weighted supervised latent factor analysis
inferred← citeshttps://doi.org/10.1016/j.engappai.2021.104381openalex-snapshot
Common and specific deep feature representation for multimode process monitoring using a novel variable-wise weighted parallel network
inferred← citeshttps://doi.org/10.1016/j.ifacol.2015.09.147openalex-snapshot
Process Monitoring Based on Recursive Probabilistic PCA for Multi-mode Process∗∗The work is supported by National Natural Science Foundation of China (No.61374047, No.61202473) and Fundamental Research Funds for Central Universities (JUSRP51322B, JUSRP111A49).
inferred← citeshttps://doi.org/10.1016/j.jfranklin.2014.12.002openalex-snapshot
Quality-relevant fault detection and diagnosis for hot strip mill process with multi-specification and multi-batch measurements
inferred← citeshttps://doi.org/10.1016/j.jfranklin.2016.11.002openalex-snapshot
Multimode process monitoring based on data-driven method
inferred← citeshttps://doi.org/10.1016/j.jprocont.2018.09.009openalex-snapshot
Sparse canonical variate analysis approach for process monitoring
inferred← citeshttps://doi.org/10.1016/j.jprocont.2019.10.010openalex-snapshot
Streaming parallel variational Bayesian supervised factor analysis for adaptive soft sensor modeling with big process data
inferred← citeshttps://doi.org/10.1016/j.jprocont.2025.103425openalex-snapshot
A robust data-driven approach for modeling industrial systems with non-stationary and sparsely sampled data streams
inferred← citeshttps://doi.org/10.1016/j.knosys.2017.03.011openalex-snapshot
Evidence fusion-based framework for condition evaluation of complex electromechanical system in process industry
inferred← citeshttps://doi.org/10.1016/j.mineng.2018.12.004openalex-snapshot
Machine learning applications in minerals processing: A review
inferred← citeshttps://doi.org/10.1016/j.neucom.2016.02.048openalex-snapshot
Fault detection for discrete-time Lipschitz nonlinear systems with signal-to-noise ratio constrained channels
inferred← citeshttps://doi.org/10.1016/j.neucom.2018.02.091openalex-snapshot
Multiphase batch process with transitions monitoring based on global preserving statistics slow feature analysis
inferred← citeshttps://doi.org/10.1021/acs.iecr.5b03902openalex-snapshot
Modeling and Monitoring for Transitions Based on Local Kernel Density Estimation and Process Pattern Construction
inferred← citeshttps://doi.org/10.1021/acs.iecr.7b00057openalex-snapshot
A Variable-Correlation-Based Sparse Modeling Method for Industrial Process Monitoring
inferred← citeshttps://doi.org/10.1021/acs.iecr.7b02618openalex-snapshot
Monitoring Batch Processes Using Sparse Parallel Factor Decomposition
inferred← citeshttps://doi.org/10.1021/ie400310qopenalex-snapshot
Fault Identification of Nonlinear Processes
inferred← citeshttps://doi.org/10.1021/ie501502topenalex-snapshot
Process Fault Detection Using Directional Kernel Partial Least Squares
inferred← citeshttps://doi.org/10.1021/ie504730xopenalex-snapshot
Monitoring of Multimode Processes Based on Subspace Decomposition
inferred← citeshttps://doi.org/10.1049/iet-epa.2017.0652openalex-snapshot
Numerical simulation of melt convection in an AC electro‐fused magnesia furnace for MgO production
inferred← citeshttps://doi.org/10.1109/access.2017.2767698openalex-snapshot
Fault Detection Based on Modified Kernel Semi-Supervised Locally Linear Embedding
inferred← citeshttps://doi.org/10.1109/access.2025.3567047openalex-snapshot
An Analysis of Semi-Supervised Machine Learning in Electrical Machines
inferred← citeshttps://doi.org/10.1109/cac.2017.8243530openalex-snapshot
Multi-block kernel probabilistic principal component analysis approach and its application for fault detection
inferred← citeshttps://doi.org/10.1109/ccdc.2015.7162600openalex-snapshot
A DKPLS reconstruction algorithm for fault diagnosis
inferred← citeshttps://doi.org/10.1109/ccdc.2018.8407597openalex-snapshot
Statistical modeling and monitoring for the between-mode transition of multimode processes
inferred← citeshttps://doi.org/10.1109/ccdc52312.2021.9602434openalex-snapshot
A multimode soft sensor method combining common feature transfer and special feature correction
inferred← citeshttps://doi.org/10.1109/cyber.2015.7288141openalex-snapshot
Monitoring and fault diagnosis for industrial process
inferred← citeshttps://doi.org/10.1109/icmic.2017.8321576openalex-snapshot
Fault detection based on global-local PCA-SVDD for multimode processes
inferred← citeshttps://doi.org/10.1109/jsen.2020.3003826openalex-snapshot
Soft Sensing of Nonlinear and Multimode Processes Based on Semi-Supervised Weighted Gaussian Regression
inferred← citeshttps://doi.org/10.1109/tase.2013.2285571openalex-snapshot
Monitoring for Nonlinear Multiple Modes Process Based on LL-SVDD-MRDA
inferred← citeshttps://doi.org/10.1109/tase.2016.2564442openalex-snapshot
Nonlinear Process Monitoring Using Regression and Reconstruction Method
inferred← citeshttps://doi.org/10.1109/tcst.2017.2718979openalex-snapshot
Fault Detection and Isolation of the Brake Cylinder System for Electric Multiple Units
inferred← citeshttps://doi.org/10.1109/tie.2014.2349479openalex-snapshot
Data-Driven Abnormal Condition Identification and Self-Healing Control System for Fused Magnesium Furnace
inferred← citeshttps://doi.org/10.1109/tie.2015.2497199openalex-snapshot
Fault diagnosis of multimode processes based on similarities
inferred← citeshttps://doi.org/10.1109/tie.2016.2637886openalex-snapshot
A Kernel Least Squares Based Approach for Nonlinear Quality-Related Fault Detection
inferred← citeshttps://doi.org/10.1109/tie.2017.2682012openalex-snapshot
Quality Relevant and Independent Two Block Monitoring Based on Mutual Information and KPCA
inferred← citeshttps://doi.org/10.1109/tie.2017.2708034openalex-snapshot
Observation and Detection for a Class of Industrial Systems
inferred← citeshttps://doi.org/10.1109/tie.2017.2708036openalex-snapshot
Comprehensive Correlation Analysis of Industrial Process
inferred← citeshttps://doi.org/10.1109/tii.2013.2281002openalex-snapshot
Guest Editorial: Special section on data-driven approaches for complex industrial systems
inferred← citeshttps://doi.org/10.1109/tii.2013.2288808openalex-snapshot
2013 Index IEEE Transactions on Industrial Informatics Vol. 9
inferred← citeshttps://doi.org/10.1109/tii.2015.2509247openalex-snapshot
Plant-Wide Industrial Process Monitoring: A Distributed Modeling Framework
inferred← citeshttps://doi.org/10.1109/tii.2016.2610839openalex-snapshot
Semisupervised JITL Framework for Nonlinear Industrial Soft Sensing Based on Locally Semisupervised Weighted PCR
inferred← citeshttps://doi.org/10.1109/tii.2018.2799600openalex-snapshot
A Common and Individual Feature Extraction-Based Multimode Process Monitoring Method With Application to the Finishing Mill Process
inferred← citeshttps://doi.org/10.1109/tii.2020.2966707openalex-snapshot
A Unified Probabilistic Monitoring Framework for Multimode Processes Based on Probabilistic Linear Discriminant Analysis
inferred← citeshttps://doi.org/10.1109/tnnls.2017.2691905openalex-snapshot
Nonlinear Decoupling Control With ANFIS-Based Unmodeled Dynamics Compensation for a Class of Complex Industrial Processes
inferred← citeshttps://doi.org/10.1155/2014/945948openalex-snapshot
Statistical Analysis of Nonlinear Processes Based on Penalty Factor
inferred← citeshttps://doi.org/10.1155/2016/9567967openalex-snapshot
Monitoring of Distillation Column Based on Indiscernibility Dynamic Kernel PCA
inferred← citeshttps://doi.org/10.1177/0954408918760903openalex-snapshot
A quality-driven stability analysis framework based on state fluctuation space model for manufacturing process
inferred← citeshttps://doi.org/10.1252/jcej.17we064openalex-snapshot
Fault Detection Based on Probabilistic Kernel Partial Least Square Regression for Industrial Processes
inferred← citeshttps://doi.org/10.20965/jaciii.2020.p0221openalex-snapshot
Assessment of Coke Oven Operating State Using Trend Analysis and Information Entropy
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