Common and specific deep feature representation for multimode process monitoring using a novel variable-wise weighted parallel network

Gaussian feature learning based on variational autoencoder for improving nonlinear process monitoring

(2019) Zehan Zhang et al.   JOURNAL OF PROCESS CONTROL

Data augmentation in fault diagnosis based on the Wasserstein generative adversarial network with gradient penalty

(2019) Xin Gao et al.   NEUROCOMPUTING

Data-driven monitoring of multimode continuous processes: A review

(2019) Marcos Quiñones-Grueiro et al.   CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS

Review and Perspectives of Data-Driven Distributed Monitoring for Industrial Plant-Wide Processes

(2019) Qingchao Jiang et al.   INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

Implementing Deep Learning for comprehensive aircraft icing and actuator/sensor fault detection/identification

(2019) Yiqun Dong   ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE

Global-and-local-structure-based neural network for fault detection

(2019) Haitao Zhao et al.   NEURAL NETWORKS

Fault detection and identification combining process measurements and statistical alarms

(2019) Matthieu Lucke et al.   CONTROL ENGINEERING PRACTICE

A multi-feature extraction technique based on principal component analysis for nonlinear dynamic process monitoring

(2019) Lingling Guo et al.   JOURNAL OF PROCESS CONTROL

Data-Driven Mode Identification and Unsupervised Fault Detection for Nonlinear Multimode Processes

(2019) Bei Wang et al.   IEEE Transactions on Industrial Informatics

Multisubspace Elastic Network for Multimode Quality-Related Process Monitoring

(2019) Bing Song et al.   IEEE Transactions on Industrial Informatics

A Common and Individual Feature Extraction-Based Multimode Process Monitoring Method with Application to the Finishing Mill Process

(2018) Kai Zhang et al.   IEEE Transactions on Industrial Informatics

Fault Diagnosis of Tennessee-Eastman Process Using Orthogonal Incremental Extreme Learning Machine Based on Driving Amount

(2018) Weidong Zou et al.   IEEE Transactions on Cybernetics

Parallel PCA–KPCA for nonlinear process monitoring

(2018) Qingchao Jiang et al.   CONTROL ENGINEERING PRACTICE

Active features extracted by deep belief network for process monitoring

(2018) Jianbo Yu et al.   ISA TRANSACTIONS

Statistical Aspects of Wasserstein Distances

(2018) Victor M. Panaretos et al.   Annual Review of Statistics and Its Application

A Distributed Networked Approach for Fault Detection of Large-Scale Systems

(2017) Francesca Boem et al.   IEEE TRANSACTIONS ON AUTOMATIC CONTROL

Multimode Process Monitoring and Fault Detection: A Sparse Modeling and Dictionary Learning Method

(2017) Xin Peng et al.   IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS

A Nonlinear Process Monitoring Approach With Locally Weighted Learning of Available Data

(2017) Shen Yin et al.   IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS

Process Monitoring for Multimodal Processes With Mode-Reachability Constraints

(2017) Muhammad Shahzad Afzal et al.   IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS

Distributed Parallel PCA for Modeling and Monitoring of Large-Scale Plant-Wide Processes With Big Data

(2017) Jinlin Zhu et al.   IEEE Transactions on Industrial Informatics

Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification

(2017) Chen Lu et al.   SIGNAL PROCESSING

A New Deep Transfer Learning Based on Sparse Auto-Encoder for Fault Diagnosis

(2017) Long Wen et al.   IEEE Transactions on Systems Man Cybernetics-Systems

A sparse PCA for nonlinear fault diagnosis and robust feature discovery of industrial processes

(2016) Hongyang Yu et al.   AICHE JOURNAL

Fault Detection and Diagnosis Based on Sparse PCA and Two-Level Contribution Plots

(2016) Lijia Luo et al.   INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

Quality-Related Statistical Process Monitoring Method Based on Global and Local Partial Least-Squares Projection

(2016) Bin Zhong et al.   INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

Distributed monitoring for large-scale processes based on multivariate statistical analysis and Bayesian method

(2016) Qingchao Jiang et al.   JOURNAL OF PROCESS CONTROL

Multimode Dynamic Process Monitoring Based on Mixture Canonical Variate Analysis Model

(2015) Qiaojun Wen et al.   INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

Robust modeling of mixture probabilistic principal component analysis and process monitoring application

(2014) Jinlin Zhu et al.   AICHE JOURNAL

Two methods of selecting Gaussian kernel parameters for one-class SVM and their application to fault detection

(2014) Yingchao Xiao et al.   KNOWLEDGE-BASED SYSTEMS

Survey on data-driven industrial process monitoring and diagnosis

(2012) S. Joe Qin   ANNUAL REVIEWS IN CONTROL

Modeling and Monitoring Between-Mode Transition of Multimodes Processes

(2012) Yingwei Zhang et al.   IEEE Transactions on Industrial Informatics

A two-step basis vector extraction strategy for multiset variable correlation analysis

(2011) Chunhui Zhao et al.   CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS

Hidden Markov models combining local and global information for nonlinear and multimodal process monitoring

(2010) Jianbo Yu   JOURNAL OF PROCESS CONTROL

Multimode process monitoring with Bayesian inference-based finite Gaussian mixture models

(2008) Jie Yu et al.   AICHE JOURNAL