Xin Peng
Citado por
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Multimode process monitoring and fault detection: A sparse modeling and dictionary learning method
X Peng, Y Tang, W Du, F Qian
IEEE Transactions on Industrial Electronics 64 (6), 4866-4875, 2017
Online performance monitoring and modeling paradigm based on just-in-time learning and extreme learning machine for a non-Gaussian chemical process
X Peng, Y Tang, W Du, F Qian
Industrial & Engineering Chemistry Research 56 (23), 6671-6684, 2017
Distributed partial least squares based residual generation for statistical process monitoring
C Tong, T Lan, H Yu, X Peng
Journal of Process Control 75, 77-85, 2019
Online quality prediction of industrial terephthalic acid hydropurification process using modified regularized slow-feature analysis
W Zhong, C Jiang, X Peng, Z Li, F Qian
Industrial & Engineering Chemistry Research 57 (29), 9604-9614, 2018
Distributed process monitoring based on canonical correlation analysis with partly-connected topology
X Peng, SX Ding, W Du, W Zhong, F Qian
Control Engineering Practice 101, 104500, 2020
A just-in-time learning based monitoring and classification method for hyper/hypocalcemia diagnosis
X Peng, Y Tang, W He, W Du, F Qian
IEEE/ACM transactions on computational biology and bioinformatics 15 (3 …, 2017
Concurrent monitoring strategy for static and dynamic deviations based on selective ensemble learning using slow feature analysis
H Hong, C Jiang, X Peng, W Zhong
Industrial & Engineering Chemistry Research 59 (10), 4620-4635, 2020
Real-time semisupervised predictive modeling strategy for industrial continuous catalytic reforming process with incomplete data using slow feature analysis
C Jiang, W Zhong, Z Li, X Peng, M Yang
Industrial & Engineering Chemistry Research 58 (37), 17406-17423, 2019
Operation optimization of hydrocracking process based on Kriging surrogate model
W Zhong, C Qiao, X Peng, Z Li, C Fan, F Qian
Control Engineering Practice 85, 34-40, 2019
Key performance index estimation based on ensemble locally weighted partial least squares and its application on industrial nonlinear processes
X Chen, W Zhong, C Jiang, Z Li, X Peng, H Cheng
Chemometrics and Intelligent Laboratory Systems 203, 104031, 2020
High-order fuzzy clustering algorithm based on multikernel mean shift
D Tan, W Zhong, C Jiang, X Peng, W He
Neurocomputing 385, 63-79, 2020
Variable-scale probabilistic just-in-time learning for soft sensor development with missing data
H Huang, X Peng, C Jiang, Z Li, W Zhong
Industrial & Engineering Chemistry Research 59 (11), 5010-5021, 2020
Robust monitoring of industrial processes using process data with outliers and missing values
L Luo, S Bao, X Peng
Chemometrics and Intelligent Laboratory Systems 192, 103827, 2019
A deep learning-based robust optimization approach for refinery planning under uncertainty
C Wang, X Peng, C Shang, C Fan, L Zhao, W Zhong
Computers & Chemical Engineering 155, 107495, 2021
Concurrent quality-relevant canonical correlation analysis for nonlinear continuous process decomposition and monitoring
X Peng, Z Li, W Zhong, F Qian, Y Tian
Industrial & Engineering Chemistry Research 59 (18), 8757-8768, 2020
Performance monitoring of non-gaussian chemical processes with modes-switching using globality-locality preserving projection
X Peng, Y Tang, W Du, F Qian
Frontiers of Chemical Science and Engineering 11 (3), 429-439, 2017
An improved knowledge evolution algorithm and its application to chemical process dynamic optimization
X Peng, R Qi, W Du, F Qian
CIESC J 63, 841-850, 2012
A Knowledge Base System for Operation Optimization: Design and Implementation Practice for the Polyethylene Process
W Zhong, C Li, X Peng, F Wan, X An, Z Tian
Engineering, 2019
Decentralized monitoring for large‐scale process using copula‐correlation analysis and Bayesian inference–based multiblock principal component analysis
Y Tian, T Hu, X Peng, W Du, H Yao
Journal of Chemometrics 33 (8), e3158, 2019
A multigroup framework for fault detection and diagnosis in large-scale multivariate systems
L Luo, X Peng, C Tong
Journal of Process Control 100, 65-79, 2021
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