Song Zhihuan
Song Zhihuan
Professor of Control Science & Engineering, Zhejiang University
Verified email at - Homepage
Cited by
Cited by
Review of recent research on data-based process monitoring
Z Ge, Z Song, F Gao
Industrial & Engineering Chemistry Research 52 (10), 3543-3562, 2013
Data mining and analytics in the process industry: The role of machine learning
Z Ge, Z Song, SX Ding, B Huang
Ieee Access 5, 20590-20616, 2017
Process monitoring based on independent component analysis− principal component analysis (ICA− PCA) and similarity factors
Z Ge, Z Song
Industrial & Engineering Chemistry Research 46 (7), 2054-2063, 2007
EEMD method and WNN for fault diagnosis of locomotive roller bearings
Y Lei, Z He, Y Zi
Expert Systems with Applications 38 (6), 7334-7341, 2011
Improved kernel PCA-based monitoring approach for nonlinear processes
Z Ge, C Yang, Z Song
Chemical Engineering Science 64 (9), 2245-2255, 2009
Online monitoring of nonlinear multiple mode processes based on adaptive local model approach
Z Ge, Z Song
Control Engineering Practice 16 (12), 1427-1437, 2008
Distributed PCA model for plant-wide process monitoring
Z Ge, Z Song
Industrial & Engineering Chemistry Research 52 (5), 1947-1957, 2013
A comparative study of just-in-time-learning based methods for online soft sensor modeling
Z Ge, Z Song
Chemometrics and Intelligent Laboratory Systems 104 (2), 306-317, 2010
Mixture Bayesian regularization method of PPCA for multimode process monitoring
Z Ge, Z Song
AIChE journal 56 (11), 2838-2849, 2010
Distributed parallel PCA for modeling and monitoring of large-scale plant-wide processes with big data
J Zhu, Z Ge, Z Song
IEEE Transactions on Industrial Informatics 13 (4), 1877-1885, 2017
Multimode process monitoring based on Bayesian method
Z Ge, Z Song
Journal of Chemometrics: A Journal of the Chemometrics Society 23 (12), 636-650, 2009
Hilbert–Huang transform based signal analysis for the characterization of gas–liquid two-phase flow
H Ding, Z Huang, Z Song, Y Yan
Flow measurement and instrumentation 18 (1), 37-46, 2007
Nonlinear process monitoring based on linear subspace and Bayesian inference
Z Ge, M Zhang, Z Song
Journal of Process Control 20 (5), 676-688, 2010
Batch process monitoring based on support vector data description method
Z Ge, F Gao, Z Song
Journal of Process Control 21 (6), 949-959, 2011
Global–local structure analysis model and its application for fault detection and identification
M Zhang, Z Ge, Z Song, R Fu
Industrial & Engineering Chemistry Research 50 (11), 6837-6848, 2011
Locally weighted kernel principal component regression model for soft sensing of nonlinear time-variant processes
X Yuan, Z Ge, Z Song
Industrial & Engineering Chemistry Research 53 (35), 13736-13749, 2014
Fault detection behavior and performance analysis of principal component analysis based process monitoring methods
H Wang, Z Song, P Li
Industrial & Engineering Chemistry Research 41 (10), 2455-2464, 2002
Weighted linear dynamic system for feature representation and soft sensor application in nonlinear dynamic industrial processes
X Yuan, Y Wang, C Yang, Z Ge, Z Song, W Gui
IEEE Transactions on Industrial Electronics 65 (2), 1508-1517, 2017
A novel fault diagnosis system using pattern classification on kernel FDA subspace
ZB Zhu, ZH Song
Expert Systems with Applications 38 (6), 6895-6905, 2011
Mixture probabilistic PCR model for soft sensing of multimode processes
Z Ge, F Gao, Z Song
Chemometrics and intelligent laboratory systems 105 (1), 91-105, 2011
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