Reconstruction-based contribution for process monitoring

2009/04/11 by Carlos F. Alcala, S. Joe Qin · 13 citations
Engineering · Decision Sciences · Chemistry · #Fault Detection and Control Systems #Advanced Statistical Process Monitoring #Spectroscopy and Chemometric Analyses

paper · doi:10.1016/j.automatica.2009.02.027

Citations

• Identification of faulty sensors using principal component analysis

Cited by

• Causal Generative Model for Root-Cause Diagnosis and Fault Propagation Analysis in Industrial Processes
• Process monitoring and dynamic fusion of complex industrial systems: A reconstruction-based Bayesian framework
• A unified model integrating Granger causality-based causal discovery and fault diagnosis in chemical processes
• Data-driven Fault Detection and Diagnosis for HVAC water chillers
• Data-Driven Robust Receding Horizon Fault Estimation
• Interpretable Fault Detection using Projections of Mutual Information Matrix
• Fault Identification of Chemical Processes Based on k-NN Variable Contribution and CNN Data Reconstruction Methods. [europepmc]
• A Comprehensive Case Study of Data-Driven Methods for Robust Aircraft Sensor Fault Isolation. [europepmc]
• Challenges in the Development of Soft Sensors for Bioprocesses: A Critical Review. [europepmc]
• Fault Detection and Diagnosis in Industrial Processes with Variational Autoencoder: A Comprehensive Study. [europepmc]
• Enhanced Multiscale Principal Component Analysis for Improved Sensor Fault Detection and Isolation. [europepmc]
• Quality-Relevant Process Monitoring with Concurrent Locality-Preserving Dynamic Latent Variable Method. [europepmc]
• Unsupervised Process Anomaly Detection and Identification Using the Leave-One-Variable-Out Approach. [europepmc]