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Distributed PCA Model for Plant-Wide Process Monitoring
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Outgoing claims: the papers this work cites, builds on, or argues with.
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Multiblock Principal Component Analysis Based on a Combined Index for Semiconductor Fault Detection and Diagnosis
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Adaptive k-Nearest-Neighbor Method for Process Monitoring
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Identification of Errors-in-Variables Models Using Dynamic Iterative Principal Component Analysis
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System Decomposition for Distributed Multivariate Statistical Process Monitoring by Performance Driven Agglomerative Clustering
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Incipient Fault Detection for Chemical Processes Using Two-Dimensional Weighted SLKPCA
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Hierarchically Distributed Monitoring for the Early Prediction of Gas Flare Events
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Distributed Supervised Fault Detection and Diagnosis for a Non-Gaussian Process
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Large-Scale Supervised Process Monitoring Based on Distributed Modified Principal Component Regression
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Review and Perspectives of Data-Driven Distributed Monitoring for Industrial Plant-Wide Processes
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Novel Distributed Alarm Visual Analysis Using Multicorrelation Block-Based PLS and Its Application to Online Root Cause Analysis
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Optimal Feature Selection for Distributed Data-Driven Process Monitoring
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Data Visualization & Clustering: Generative Topographic Mapping Similarity Assessment Allied to Graph Theory Clustering
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Shrinking Principal Component Analysis for Enhanced Process Monitoring and Fault Isolation
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Multiblock Independent Component Analysis Integrated with Hellinger Distance and Bayesian Inference for Non-Gaussian Plant-Wide Process Monitoring
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Multivariate Statistical Process Control Method Including Soft Sensors for Both Early and Accurate Fault Detection
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Joint Probability Density and Double-Weighted Independent Component Analysis for Multimode Non-Gaussian Process Monitoring
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Plant‐wide processes monitoring and fault tracing based on causal graphical model
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A novel distributed fine-grained energy consumption monitoring for large-scale nonlinear dynamic industrial processes
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Data-driven process decomposition and robust online distributed modelling for large-scale processes
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Incorporation of process-specific structure in statistical process monitoring: A review
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Independent and Related Variable Fault Detection Based on Information Concentrated Variational Auto-encoder
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Data-Driven Optimized Distributed Dynamic PCA for Efficient Monitoring of Large-Scale Dynamic Processes
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Optimal Variable Transmission for Distributed Local Fault Detection Incorporating RA and Evolutionary Optimization
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Double Layer Distributed Process Monitoring Based on Hierarchical Multi-Block Decomposition
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A Novel Decentralized Weighted ReliefF-PCA Method for Fault Detection
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Fault Relevant Variable Selection for Fault Diagnosis
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Variable Sub-Region Canonical Variate Analysis for Dynamic Process Monitoring
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Plant-Wide Process Monitoring Strategy Based on Complex Network and Bayesian Inference-Based Multi-Block Principal Component Analysis
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A Novel Multi-Mode Bayesian Method for the Process Monitoring and Fault Diagnosis of Coal Mills
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Dynamic Decentralized Monitoring for Large-Scale Industrial Processes Using Multiblock Canonical Variate Analysis Based Regression
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Process decomposition and distributed fault detection of large-scale industrial processes
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Nonlinear chemical process monitoring using decentralized kernel principal component analysis and Bayesian inference
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Dual-step subspace partition for process monitoring
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A Distributed Local Fault Detection Method Based on Kernel Canonical Correlation Analysis and Particle Swarm Optimization
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Fault diagnosis of rail vehicle suspension system based on distributed DPCA
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Distributed Gaussian mixture model for monitoring multimode plant-wide process
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Multimode process fault detection method using local neighborhood standardization based multi-block principal component analysis
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Performance-Driven Subsystems Decomposition for Distributed Fault Detection
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Thermal Power Plant Process Monitoring using Mutual Information and Distributed Statistical Model
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Online Monitoring of Time-varying Process Using Probabilistic Principal Component Analysis
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Causal analysis enhanced stacked denoising autoencoder for closed-loop industrial process monitoring
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K-STGCT: A Causal-Enhanced Spatio-Temporal Gated Convolutional Transformer with KAN-based Positional Encoding for Industrial Soft Sensor Forecasting
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Decentralized and Dynamic Fault Detection Using PCA and Bayesian Inference
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Decentralized DPCA Model for Large-Scale Processes Monitoring
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Distributed System Monitoring and Fault Diagnosis Based on Causal Graphical Model
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Research on Fault Detection Method Based on Improved Euclidean Distance Control
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The flywheel fault detection based on Kernel principal component analysis
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An Evaluative Study on IoT Ecosystem for Smart Predictive Maintenance (IoT-SPM) in Manufacturing: Multiview Requirements and Data Quality
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Fault detection with distributed PCA methods in water distribution networks
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Performance Supervised Plant-Wide Process Monitoring in Industry 4.0: A Roadmap
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A Novel Decentralized Process Monitoring Scheme Using a Modified Multiblock PCA Algorithm
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Complex System Monitoring Based on Distributed Least Squares Method
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Data-Driven Communication Efficient Distributed Monitoring for Multiunit Industrial Plant-Wide Processes
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Quality-Related Spatio-Temporal Information Analytics-Based Multiunit Synergetic Monitoring for Plant-Wide Industrial Processes
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A Novel Hybrid Method Integrating ICA-PCA With Relevant Vector Machine for Multivariate Process Monitoring
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Neighborhood Variational Bayesian Multivariate Analysis for Distributed Process Monitoring With Missing Data
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Hierarchical Bayesian Network Modeling Framework for Large-Scale Process Monitoring and Decision Making
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Distributed Dynamic Process Monitoring Based on Minimal Redundancy Maximal Relevance Variable Selection and Bayesian Inference
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Performance-Driven Distributed PCA Process Monitoring Based on Fault-Relevant Variable Selection and Bayesian Inference
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Decentralized Monitoring of Dynamic Processes Based on Dynamic Feature Selection and Informative Fault Pattern Dissimilarity
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Bayesian Fault Diagnosis With Asynchronous Measurements and Its Application in Networked Distributed Monitoring
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Quality Relevant and Independent Two Block Monitoring Based on Mutual Information and KPCA
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Data-Driven Distributed Local Fault Detection for Large-Scale Processes Based on the GA-Regularized Canonical Correlation Analysis
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Performance-Indicator-Oriented Concurrent Subspace Process Monitoring Method
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Plant-Wide Industrial Process Monitoring: A Distributed Modeling Framework
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Distributed Parallel PCA for Modeling and Monitoring of Large-Scale Plant-Wide Processes With Big Data
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A Distributed Canonical Correlation Analysis-Based Fault Detection Method for Plant-Wide Process Monitoring
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Novel Transformer Based on Gated Convolutional Neural Network for Dynamic Soft Sensor Modeling of Industrial Processes
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Empowering IoT Predictive Maintenance Solutions With AI: A Distributed System for Manufacturing Plant-Wide Monitoring
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Convolutional Neural Network Based Feature Learning for Large-Scale Quality-Related Process Monitoring
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Plant-Wide Process Fine-Scale Monitoring via Distributed Static Magnitude-Dynamic Difference
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