相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。RNN- and LSTM-Based Soft Sensors Transferability for an Industrial Process
Francesco Curreri et al.
SENSORS (2021)
A just-in-time modeling approach for multimode soft sensor based on Gaussian mixture variational autoencoder
Fan Guo et al.
COMPUTERS & CHEMICAL ENGINEERING (2021)
Prediction of material removal rate in chemical mechanical polishing via residual convolutional neural network
Jiusi Zhang et al.
CONTROL ENGINEERING PRACTICE (2021)
A Layer-Wise Data Augmentation Strategy for Deep Learning Networks and Its Soft Sensor Application in an Industrial Hydrocracking Process
Xiaofeng Yuan et al.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2021)
Refining data-driven soft sensor modeling framework with variable time reconstruction
Le Yao et al.
JOURNAL OF PROCESS CONTROL (2020)
A dynamic CNN for nonlinear dynamic feature learning in soft sensor modeling of industrial process data
Xiaofeng Yuan et al.
CONTROL ENGINEERING PRACTICE (2020)
Data-driven dynamic inferential sensors based on causality analysis
Liang Cao et al.
CONTROL ENGINEERING PRACTICE (2020)
A novel scoring function based on family transfer entropy for Bayesian networks learning and its application to industrial alarm systems
Qian-Qian Meng et al.
JOURNAL OF PROCESS CONTROL (2019)
Hybrid causal analysis combining a nonparametric multiplicative regression causality estimator with process connectivity information
R. Landman et al.
CONTROL ENGINEERING PRACTICE (2019)
Layered online data reconciliation strategy with multiple modes for industrial processes
Sen Xie et al.
CONTROL ENGINEERING PRACTICE (2018)
Adaptive soft sensors for quality prediction under the framework of Bayesian network
Ziwei Liu et al.
CONTROL ENGINEERING PRACTICE (2018)
Deep Learning-Based Feature Representation and Its Application for Soft Sensor Modeling With Variable-Wise Weighted SAE
Xiaofeng Yuan et al.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2018)
Regression on dynamic PLS structures for supervised learning of dynamic data
Yining Dong et al.
JOURNAL OF PROCESS CONTROL (2018)
A novel dynamic PCA algorithm for dynamic data modeling and process monitoring
Yining Dong et al.
JOURNAL OF PROCESS CONTROL (2018)
Parallel PCA-KPCA for nonlinear process monitoring
Qingchao Jiang et al.
CONTROL ENGINEERING PRACTICE (2018)
Dynamic-Inner Canonical Correlation and Causality Analysis for High Dimensional Time Series Data
Yining Dong et al.
IFAC PAPERSONLINE (2018)
A Modified Dynamic PLS for Quality Related Monitoring of Fractionation Processes
Xue Xu et al.
IFAC PAPERSONLINE (2018)
Soft Sensor Development for Multimode Processes Based on Semisupervised Gaussian Mixture Models
Weiming Shao et al.
IFAC PAPERSONLINE (2018)
Refined convergent cross-mapping for disturbance propagation analysis of chemical processes
Lei Luo et al.
COMPUTERS & CHEMICAL ENGINEERING (2017)
Causal inference by using invariant prediction: identification and confidence intervals
Jonas Peters et al.
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY (2016)
Probabilistic slow feature analysis-based representation learning from massive process data for soft sensor modeling
Chao Shang et al.
AICHE JOURNAL (2015)
Concurrent monitoring of operating condition deviations and process dynamics anomalies with slow feature analysis
Chao Shang et al.
AICHE JOURNAL (2015)
Dynamic-Inner Partial Least Squares for Dynamic Data Modeling
Yining Dong et al.
IFAC PAPERSONLINE (2015)
Mixture Semisupervised Principal Component Regression Model and Soft Sensor Application
Zhiqiang Ge et al.
AICHE JOURNAL (2014)
An Iterative Two-Level Optimization Method for the Modeling of Wiener Structure Nonlinear Dynamic Soft Sensors
Xinqing Gao et al.
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH (2014)
Locally Weighted Kernel Principal Component Regression Model for Soft Sensing of Nonlinear Time-Variant Processes
Xiaofeng Yuan et al.
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH (2014)
Root cause diagnosis of plant-wide oscillations using Granger causality
Tao Yuan et al.
JOURNAL OF PROCESS CONTROL (2014)
Data-driven soft sensor development based on deep learning technique
Chao Shang et al.
JOURNAL OF PROCESS CONTROL (2014)
Direct Causality Detection via the Transfer Entropy Approach
Ping Duan et al.
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY (2013)
SIGNED DIRECTED GRAPH BASED MODELING AND ITS VALIDATION FROM PROCESS KNOWLEDGE AND PROCESS DATA
Fan Yang et al.
INTERNATIONAL JOURNAL OF APPLIED MATHEMATICS AND COMPUTER SCIENCE (2012)
Detecting Causality in Complex Ecosystems
George Sugihara et al.
SCIENCE (2012)
Mixture probabilistic PCR model for soft sensing of multimode processes
Zhiqiang Ge et al.
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS (2011)
A MATLAB toolbox for Granger causal connectivity analysis
Anil K. Seth
JOURNAL OF NEUROSCIENCE METHODS (2010)
ANN-based soft-sensor for real-time process monitoring and control of an industrial polymerization process
J. C. B. Gonzaga et al.
COMPUTERS & CHEMICAL ENGINEERING (2009)
Root cause diagnosis of plant-wide oscillations using the concept of adjacency matrix
Hailei Jiang et al.
JOURNAL OF PROCESS CONTROL (2009)
Effects of noise on transfer entropy estimation for damage detection
L. A. Overbey et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2009)
Data-based process monitoring, process control, and quality improvement: Recent developments and applications in steel industry
Manabu Kano et al.
COMPUTERS & CHEMICAL ENGINEERING (2008)
A practical method for identifying the propagation path of plant-wide disturbances
Margret Bauer et al.
JOURNAL OF PROCESS CONTROL (2008)
Process monitoring using causal map and multivariate statistics: fault detection and identification
LH Chiang et al.
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS (2003)
Measuring information transfer
T Schreiber
PHYSICAL REVIEW LETTERS (2000)
Inferential control system of distillation compositions using dynamic partial least squares regression
M Kano et al.
JOURNAL OF PROCESS CONTROL (2000)