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。Dynamic nonlinear batch process fault detection and identification based on two-directional dynamic kernel slow feature analysis
Hanyuan Zhang et al.
CANADIAN JOURNAL OF CHEMICAL ENGINEERING (2021)
RNN- and LSTM-Based Soft Sensors Transferability for an Industrial Process
Francesco Curreri et al.
SENSORS (2021)
Decentralized dynamic process monitoring based on manifold regularized slow feature analysis
Xue Xu et al.
JOURNAL OF PROCESS CONTROL (2021)
Industrial Semi-Supervised Dynamic Soft-Sensor Modeling Approach Based on Deep Relevant Representation Learning
Jean Mario Moreira de Lima et al.
SENSORS (2021)
Novel soft sensor development using echo state network integrated with singular value decomposition: Application to complex chemical processes
Yan-Lin He et al.
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS (2020)
Locally weighted slow feature regression for nonlinear dynamic soft sensor modeling and its application to an industrial hydrocracking process
Xiaofeng Yuan et al.
MEASUREMENT SCIENCE AND TECHNOLOGY (2020)
Nonlinear process modelling using echo state networks optimised by covariance matrix adaption evolutionary strategy
Kai Liu et al.
COMPUTERS & CHEMICAL ENGINEERING (2020)
Modeling data-driven sensor with a novel deep echo state network
Ying-Chun Bo et al.
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS (2020)
Integrating dynamic slow feature analysis with neural networks for enhancing soft sensor performance
Jeremiah Corrigan et al.
COMPUTERS & CHEMICAL ENGINEERING (2020)
Hierarchical Quality-Relevant Feature Representation for Soft Sensor Modeling: A Novel Deep Learning Strategy
Xiaofeng Yuan et al.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2020)
Soft sensor modeling for fraction yield of crude oil based on ensemble deep learning
Ling Yi et al.
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS (2020)
A subspace ensemble regression model based slow feature for soft sensing application
Qiong Jia et al.
CHINESE JOURNAL OF CHEMICAL ENGINEERING (2020)
Variants of slow feature analysis framework for automatic detection and isolation of multiple oscillations in coupled control loops
Jie Wang et al.
COMPUTERS & CHEMICAL ENGINEERING (2020)
Slow feature analysis-independent component analysis based integrated monitoring approach for industrial processes incorporating dynamic and static characteristics
Jian Huang et al.
CONTROL ENGINEERING PRACTICE (2020)
Soft Sensors Based on Deep Neural Networks for Applications in Security and Safety
Maria Gabriella Xibilia et al.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2020)
Soft-sensor modeling for l-lysine fermentation process based on hybrid ICS-MLSSVM
Bo Wang et al.
SCIENTIFIC REPORTS (2020)
Trajectory-based operation monitoring of transition procedure in multimode process
Zhaojing Wang et al.
JOURNAL OF PROCESS CONTROL (2020)
Extreme Learning Machine Soft-Sensor Model With Different Activation Functions on Grinding Process Optimized by Improved Black Hole Algorithm
W. Xie et al.
IEEE ACCESS (2020)
Slow-Feature-Analysis-Based Batch Process Monitoring With Comprehensive Interpretation of Operation Condition Deviation and Dynamic Anomaly
Shumei Zhang et al.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2019)
A new soft-sensor algorithm with concurrent consideration of slowness and quality interpretation for dynamic chemical process
Yan Qin et al.
CHEMICAL ENGINEERING SCIENCE (2019)
Dynamic Soft Sensor Development Based on Convolutional Neural Networks
Kangcheng Wang et al.
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH (2019)
Comprehensive process decomposition for closed-loop process monitoring with quality-relevant slow feature analysis
Yan Qin et al.
JOURNAL OF PROCESS CONTROL (2019)
Recursive Exponential Slow Feature Analysis for Fine-Scale Adaptive Processes Monitoring With Comprehensive Operation Status Identification
Wanke Yu et al.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2019)
Simultaneous Static and Dynamic Analysis for Fine-Scale Identification of Process Operation Statuses
Shumei Zhang et al.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2019)
Online monitoring of performance variations and process dynamic anomalies with performance-relevant full decomposition of slow feature analysis
Jiale Zheng et al.
JOURNAL OF PROCESS CONTROL (2019)
A slow independent component analysis algorithm for time series feature extraction with the concurrent consideration of high-order statistic and slowness
Liangjun Feng et al.
JOURNAL OF PROCESS CONTROL (2019)
Nonlinear Dynamic Soft Sensor Modeling With Supervised Long Short-Term Memory Network
Xiaofeng Yuan et al.
IEEE Transactions on Industrial Informatics (2019)
A deep learning based data driven soft sensor for bioprocesses
Vineet Gopakumar et al.
BIOCHEMICAL ENGINEERING JOURNAL (2018)
A new active learning strategy for soft sensor modeling based on feature reconstruction and uncertainty evaluation
Qifeng Tang et al.
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS (2018)
Temperature Prediction Model for Roller Kiln by ALD-Based Double Locally Weighted Kernel Principal Component Regression
Ning Chen et al.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2018)
Batch process fault detection and identification based on discriminant global preserving kernel slow feature analysis
Hanyuan Zhang et al.
ISA TRANSACTIONS (2018)
A Full-Condition Monitoring Method for Nonstationary Dynamic Chemical Processes with Cointegration and Slow Feature Analysis
Chunhui Zhao et al.
AICHE JOURNAL (2018)
Batch Process Monitoring Based on Multiway Global Preserving Kernel Slow Feature Analysis
Hanyuan Zhang et al.
IEEE ACCESS (2017)
Slow feature analysis for monitoring and diagnosis of control performance
Chao Shang et al.
JOURNAL OF PROCESS CONTROL (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)
Process fault detection based on dynamic kernel slow feature analysis
Ni Zhang et al.
COMPUTERS & ELECTRICAL ENGINEERING (2015)
Multisource Data Ensemble Modeling for Clinker Free Lime Content Estimate in Rotary Kiln Sintering Processes
Weitao Li et al.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS (2015)
Locally Weighted Kernel Principal Component Regression Model for Soft Sensing of Nonlinear Time-Variant Processes
Xiaofeng Yuan et al.
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH (2014)
Progressive multi-block modelling for enhanced fault isolation in batch processes
Jeong Jin Hong 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)
Development of soft sensor by incorporating the delayed infrequent and irregular measurements
Yafeng Guo et al.
JOURNAL OF PROCESS CONTROL (2014)
A method for multiphase batch process monitoring based on auto phase identification
Wei Sun et al.
JOURNAL OF PROCESS CONTROL (2011)
Data-driven Soft Sensors in the process industry
Petr Kadlec et al.
COMPUTERS & CHEMICAL ENGINEERING (2009)
Extreme learning machine: Theory and applications
Guang-Bin Huang et al.
NEUROCOMPUTING (2006)
Fault detection of batch processes using multiway kernel principal component analysis
JM Lee et al.
COMPUTERS & CHEMICAL ENGINEERING (2004)
A modular simulation package for fed-batch fermentation:: penicillin production
G Birol et al.
COMPUTERS & CHEMICAL ENGINEERING (2002)
Slow feature analysis: Unsupervised learning of invariances
L Wiskott et al.
NEURAL COMPUTATION (2002)
Soft sensors development for on-line bioreactor state estimation
AJ de Assis et al.
COMPUTERS & CHEMICAL ENGINEERING (2000)