A Novel Mutual Information and Partial Least Squares Approach for Quality-Related and Quality-Unrelated Fault Detection
暂无分享,去创建一个
[1] Yang Tao,et al. Multisubspace Orthogonal Canonical Correlation Analysis for Quality-Related Plant-Wide Process Monitoring , 2021, IEEE Transactions on Industrial Informatics.
[2] Guang Wang,et al. Quality-Related Fault Detection and Diagnosis Based on Total Principal Component Regression Model , 2018, IEEE Access.
[3] Xuefeng Yan,et al. Quality-Driven Autoencoder for Nonlinear Quality-Related and Process-Related Fault Detection Based on Least-Squares Regularization and Enhanced Statistics , 2020 .
[4] Steven X. Ding,et al. A Review on Basic Data-Driven Approaches for Industrial Process Monitoring , 2014, IEEE Transactions on Industrial Electronics.
[5] S. Joe Qin,et al. Statistical process monitoring: basics and beyond , 2003 .
[6] Furong Gao,et al. Data-Driven Batch-End Quality Modeling and Monitoring Based on Optimized Sparse Partial Least Squares , 2020, IEEE Transactions on Industrial Electronics.
[7] Steven X. Ding,et al. Data-driven design of monitoring and diagnosis systems for dynamic processes: A review of subspace technique based schemes and some recent results , 2014 .
[8] H. Shi,et al. Parallel quality-related dynamic principal component regression method for chemical process monitoring , 2019, Journal of Process Control.
[9] Huijun Gao,et al. Data-Driven Process Monitoring Based on Modified Orthogonal Projections to Latent Structures , 2016, IEEE Transactions on Control Systems Technology.
[10] Kaixiang Peng,et al. A Novel Scheme for Key Performance Indicator Prediction and Diagnosis With Application to an Industrial Hot Strip Mill , 2013, IEEE Transactions on Industrial Informatics.
[11] R. Asorey-Cacheda,et al. A survey on non-linear optimization problems in wireless sensor networks , 2017, J. Netw. Comput. Appl..
[12] Xuefeng Yan,et al. Quality-Driven Kernel Projection to Latent Structure Model for Nonlinear Process Monitoring , 2019, IEEE Access.
[13] Hongbo Shi,et al. A Novel Dynamic Weight Principal Component Analysis Method and Hierarchical Monitoring Strategy for Process Fault Detection and Diagnosis , 2020, IEEE Transactions on Industrial Electronics.
[14] E. F. Vogel,et al. A plant-wide industrial process control problem , 1993 .
[15] Youqing Wang,et al. Key-Performance-Indicator-Related Process Monitoring Based on Improved Kernel Partial Least Squares , 2021, IEEE Transactions on Industrial Electronics.
[16] Shu-Kai S. Fan,et al. Multiple-input multiple-output double exponentially weighted moving average controller using partial least squares , 2010 .
[17] Donghua Zhou,et al. Total projection to latent structures for process monitoring , 2009 .
[18] Jian Hou,et al. An Improved Principal Component Regression for Quality-Related Process Monitoring of Industrial Control Systems , 2017, IEEE Access.
[19] Jie Yu. Multiway Gaussian Mixture Model Based Adaptive Kernel Partial Least Squares Regression Method for Soft Sensor Estimation and Reliable Quality Prediction of Nonlinear Multiphase Batch Processes , 2012 .
[20] Shu-Kai S. Fan,et al. An integrated advanced process control framework using run-to-run control, virtual metrology and fault detection , 2013 .
[21] Zhiqiang Ge,et al. Review on data-driven modeling and monitoring for plant-wide industrial processes , 2017 .
[22] Donghua Zhou,et al. Geometric properties of partial least squares for process monitoring , 2010, Autom..