Multimode Process Monitoring Based on Modified Density Peak Clustering and Parallel Variational Autoencoder

Multimode process monitoring based on hierarchical mode identification and stacked denoising autoencoder

(2022) Huihui Gao et al.   CHEMICAL ENGINEERING SCIENCE

Anomaly Detection with GRU Based Bi-autoencoder for Industrial Multimode Process

(2022) Xinyao Xu et al.   INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS

A method of two-stage clustering learning based on improved DBSCAN and density peak algorithm

(2021) Mingyang Li et al.   COMPUTER COMMUNICATIONS

A just-in-time modeling approach for multimode soft sensor based on Gaussian mixture variational autoencoder

(2021) Fan Guo et al.   COMPUTERS & CHEMICAL ENGINEERING

An Extended Tennessee Eastman Simulation Dataset for Fault-Detection and Decision Support Systems

(2021) Christopher Reinartz et al.   COMPUTERS & CHEMICAL ENGINEERING

Gaussian mixture deep dynamic latent variable model with application to soft sensing for multimode industrial processes

(2021) Jingyun Xu et al.   APPLIED SOFT COMPUTING

A process monitoring and fault isolation framework based on variational autoencoders and branch and bound method

(2021) Peng Tang et al.   JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS

A multimode process monitoring strategy via improved variational inference Gaussian mixture model based on locality preserving projections

(2021) Qingxiu Guo et al.   TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL

Density peaks clustering‐based steady/transition mode identification and monitoring of multimode processes

(2020) Ying Zheng et al.   CANADIAN JOURNAL OF CHEMICAL ENGINEERING

Process monitoring using principal component analysis and stacked autoencoder for linear and nonlinear coexisting industrial processes

(2020) Jiangsheng Li et al.   Journal of the Taiwan Institute of Chemical Engineers

Local component based principal component analysis model for multimode process monitoring

(2020) Yuan Li et al.   CHINESE JOURNAL OF CHEMICAL ENGINEERING

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

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

Data-driven monitoring of multimode continuous processes: A review

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

A Novel Dynamic Weight Principal Component Analysis Method and Hierarchical Monitoring Strategy for Process Fault Detection and Diagnosis

(2019) Yang Tao et al.   IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS

Multimode processes monitoring based on hierarchical mode division and subspace decomposition

(2018) Shuai Li et al.   CANADIAN JOURNAL OF CHEMICAL ENGINEERING

Fault Detection Strategy based on Weighted Distance of k Nearest Neighbors for Semiconductor Manufacturing Processes

(2018) Cheng Zhang et al.   IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING

Ensemble learning model based on selected diverse principal component analysis models for process monitoring

(2018) Zhichao Li et al.   JOURNAL OF CHEMOMETRICS

Temporal-Spatial Global Locality Projections for Multimode Process Monitoring

(2018) Bing Song et al.   IEEE Access

ONLINE OPERATING PERFORMANCE EVALUATION FOR THE PLANT-WIDE INDUSTRIAL PROCESS BASED ON A THREE-LEVEL AND MULTI-BLOCK METHOD

(2018) Yuqing Chang et al.   CANADIAN JOURNAL OF CHEMICAL ENGINEERING

Phase Partition and Phase-Based Process Monitoring Methods for Multiphase Batch Processes with Uneven Durations

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

Novel Monitoring Strategy Combining the Advantages of the Multiple Modeling Strategy and Gaussian Mixture Model for Multimode Processes

(2015) Shumei Zhang et al.   INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

Quality-related prediction and monitoring of multi-mode processes using multiple PLS with application to an industrial hot strip mill

(2015) Kaixiang Peng et al.   NEUROCOMPUTING

Multimode Process Fault Detection Using Local Neighborhood Similarity Analysis

(2014) Xiaogang Deng et al.   CHINESE JOURNAL OF CHEMICAL ENGINEERING

Statistical Monitoring of Wastewater Treatment Plants Using Variational Bayesian PCA

(2014) Yiqi Liu et al.   INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

Clustering by fast search and find of density peaks

(2014) A. Rodriguez et al.   SCIENCE

Multimode Process Monitoring Based on Fuzzy C-means in Locality Preserving Projection Subspace

(2013) Xiang XIE et al.   CHINESE JOURNAL OF CHEMICAL ENGINEERING

Review of Recent Research on Data-Based Process Monitoring

(2013) Zhiqiang Ge et al.   INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

Survey on data-driven industrial process monitoring and diagnosis

(2012) S. Joe Qin   ANNUAL REVIEWS IN CONTROL

A novel local neighborhood standardization strategy and its application in fault detection of multimode processes

(2012) Hehe Ma et al.   CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS

Dynamic Multimode Process Modeling and Monitoring Using Adaptive Gaussian Mixture Models

(2012) Xiang Xie et al.   INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

Kernel k-means clustering based local support vector domain description fault detection of multimodal processes

(2011) Issam Ben Khediri et al.   EXPERT SYSTEMS WITH APPLICATIONS

Adaptive Mahalanobis Distance and $k$-Nearest Neighbor Rule for Fault Detection in Semiconductor Manufacturing

(2010) Ghislain Verdier et al.   IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING

Multimode process monitoring based on Bayesian method

(2009) Zhiqiang Ge et al.   JOURNAL OF CHEMOMETRICS

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

(2008) Jie Yu et al.   AICHE JOURNAL

Online monitoring of nonlinear multiple mode processes based on adaptive local model approach

(2008) Zhiqiang Ge et al.   CONTROL ENGINEERING PRACTICE