Qingchao Jiang

Orcid: 0000-0002-3402-9018

According to our database1, Qingchao Jiang authored at least 28 papers between 2016 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Hierarchical Fault Root Cause Identification in Plant-Wide Processes Using Distributed Direct Causality Analysis.
IEEE Trans. Ind. Informatics, March, 2024

2023
A Zoning Search-Based Multimodal Multi-Objective Brain Storm Optimization Algorithm for Multimodal Multi-Objective Optimization.
Algorithms, July, 2023

Optimized Gaussian-Process-Based Probabilistic Latent Variable Modeling Framework for Distributed Nonlinear Process Monitoring.
IEEE Trans. Syst. Man Cybern. Syst., May, 2023

Data-Driven Soft Sensing for Batch Processes Using Neural Network-Based Deep Quality-Relevant Representation Learning.
IEEE Trans. Artif. Intell., 2023

2022
Distributed Robust Process Monitoring Based on Optimized Denoising Autoencoder With Reinforcement Learning.
IEEE Trans. Instrum. Meas., 2022

Data-Driven Communication Efficient Distributed Monitoring for Multiunit Industrial Plant-Wide Processes.
IEEE Trans Autom. Sci. Eng., 2022

Dynamic nonlinear process monitoring based on dynamic correlation variable selection and kernel principal component regression.
J. Frankl. Inst., 2022

2021
Local-Global Modeling and Distributed Computing Framework for Nonlinear Plant-Wide Process Monitoring With Industrial Big Data.
IEEE Trans. Neural Networks Learn. Syst., 2021

Imbalanced Classification Based on Minority Clustering Synthetic Minority Oversampling Technique With Wind Turbine Fault Detection Application.
IEEE Trans. Ind. Informatics, 2021

Distributed-ensemble stacked autoencoder model for non-linear process monitoring.
Inf. Sci., 2021

2020
Data-Driven Two-Dimensional Deep Correlated Representation Learning for Nonlinear Batch Process Monitoring.
IEEE Trans. Ind. Informatics, 2020

Data-Driven Batch-End Quality Modeling and Monitoring Based on Optimized Sparse Partial Least Squares.
IEEE Trans. Ind. Electron., 2020

Deep Discriminative Representation Learning for Nonlinear Process Fault Detection.
IEEE Trans Autom. Sci. Eng., 2020

Deep relevant representation learning for soft sensing.
Inf. Sci., 2020

2019
Learning Deep Correlated Representations for Nonlinear Process Monitoring.
IEEE Trans. Ind. Informatics, 2019

Multiobjective Two-Dimensional CCA-Based Monitoring for Successive Batch Processes With Industrial Injection Molding Application.
IEEE Trans. Ind. Electron., 2019

Neighborhood Variational Bayesian Multivariate Analysis for Distributed Process Monitoring With Missing Data.
IEEE Trans. Control. Syst. Technol., 2019

Multivariate Statistical Monitoring of Key Operation Units of Batch Processes Based on Time-Slice CCA.
IEEE Trans. Control. Syst. Technol., 2019

Multimode Process Monitoring Using Variational Bayesian Inference and Canonical Correlation Analysis.
IEEE Trans Autom. Sci. Eng., 2019

Quality-Driven Kernel Projection to Latent Structure Model for Nonlinear Process Monitoring.
IEEE Access, 2019

2018
Joint-Individual Monitoring of Parallel-Running Batch Processes Based on MCCA.
IEEE Access, 2018

Optimal Variable Transmission for Distributed Local Fault Detection Incorporating RA and Evolutionary Optimization.
IEEE Access, 2018

2017
Data-Driven Distributed Local Fault Detection for Large-Scale Processes Based on the GA-Regularized Canonical Correlation Analysis.
IEEE Trans. Ind. Electron., 2017

Data-Driven Optimized Distributed Dynamic PCA for Efficient Monitoring of Large-Scale Dynamic Processes.
IEEE Access, 2017

2016
Performance-Driven Distributed PCA Process Monitoring Based on Fault-Relevant Variable Selection and Bayesian Inference.
IEEE Trans. Ind. Electron., 2016

Bayesian Fault Diagnosis With Asynchronous Measurements and Its Application in Networked Distributed Monitoring.
IEEE Trans. Ind. Electron., 2016

GMM and optimal principal components-based Bayesian method for multimode fault diagnosis.
Comput. Chem. Eng., 2016

Independent component analysis model utilizing de-mixing information for improved non-Gaussian process monitoring.
Comput. Ind. Eng., 2016


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