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IEEE Transactions on Industrial Electronics, ISSN 0278-0046, 1/2020, pp. 1 - 1
The process monitoring performance of traditional kernel-based modeling methods strongly depends on the appropriate selection of the kernel function which... 
semi-supervised modeling | nonlinear process | Data-driven process monitoring | kernel learning
Journal Article
IEEE Transactions on Industrial Informatics, ISSN 1551-3203, 08/2017, Volume 13, Issue 4, pp. 1877 - 1885
Journal Article
Industrial & Engineering Chemistry Research, ISSN 0888-5885, 03/2013, Volume 52, Issue 10, pp. 3543 - 3562
Data-based process monitoring has become a key technology in process industries for safety, quality, and operation efficiency enhancement. This paper provides... 
ENGINEERING, CHEMICAL | DIAGNOSIS | QUALITY PREDICTION | DYNAMIC PCA | PRINCIPAL COMPONENT ANALYSIS | PROCESS FAULT-DETECTION | NEURAL-NETWORK | FED-BATCH PROCESS | PARTIAL LEAST-SQUARES | QUANTITATIVE MODEL | STATISTICAL PROCESS-CONTROL | Industrial engineering | Terminology | Safety | Monitoring
Journal Article
Sensors, ISSN 1424-8220, 11/2018, Volume 18, Issue 11, p. 3968
Because of multiple manufacturing phases or operating conditions, a great many industrial processes work with multiple modes. In addition, it is inevitable... 
multimode process | Student’s-t mixture regression | expectation maximization | robust soft sensor | Gaussian mixture model
Journal Article
Neurocomputing, ISSN 0925-2312, 12/2019, Volume 369, pp. 11 - 28
Owing to the requirements of various product grades or operation conditions, most of industrial processes work with multiple modes. Gaussian mixture regression... 
Multi-mode industrial processes | Gaussian mixture regression | Robust inferential sensor | Variational Bayesian expectation maximization | Variational Bayesian Student’s-t mixture regression
Journal Article
IEEE Transactions on Control Systems Technology, ISSN 1063-6536, 05/2017, Volume 25, Issue 3, pp. 1092 - 1099
Journal Article
Journal of Process Control, ISSN 0959-1524, 01/2020, Volume 85, pp. 199 - 213
•The data-driven design for control performance diagnosis is considered.•The sparse method of the generalized eigen-decomposition issue is proposed.•Sparse... 
Generalized eigendecomposition | Contribution plots | Control performance diagnosis | Sparse loadings
Journal Article
Control Engineering Practice, ISSN 0967-0661, 11/2019, Volume 92, p. 104155
Data-driven inferential sensor has been widely adopted to estimate key quality relevant variables. However, industrial dataset usually presents many... 
Heavy-tailed distribution outliers | Robust inferential sensor | Bayesian Student’s t mixture regression | Variational inference | Semi-supervised learning
Journal Article
American Institute of Chemical Engineers. AIChE Journal, ISSN 0001-1541, 10/2013, Volume 59, Issue 10, p. 3702
  A new probabilistic monitoring method for batch processes that have multiple operating conditions is described. Particularly, for multiphase batch processes,... 
Fault diagnosis | Operations management | Batch processes | Probability | Fermentation | Bayesian analysis
Journal Article
Industrial & Engineering Chemistry Research, ISSN 0888-5885, 02/2013, Volume 52, Issue 5, pp. 1947 - 1957
For plant-wide process monitoring, most traditional multiblock methods are under the assumption that some process knowledge should be incorporated for dividing... 
ENGINEERING, CHEMICAL | MULTIBLOCK | DIAGNOSIS | FAULT-DETECTION | IDENTIFICATION | Fault diagnosis | Construction | Automation | Computer simulation | Industrial engineering | Subspaces | Monitoring | Principal component analysis
Journal Article
IEEE Transactions on Industrial Informatics, ISSN 1551-3203, 04/2017, Volume 13, Issue 2, pp. 532 - 541
Journal Article
IEEE Transactions on Cybernetics, ISSN 2168-2267, 11/2019, pp. 1 - 14
Soft sensors have been widely accepted for online estimating key quality-related variables in industrial processes. The Gaussian mixture models (GMM) is one of... 
soft sensor | Bayesian Gaussian mixture models (GMMs) | non-Gaussian industrial processes | semisupervised learning | weighted variational inference (WVI)
Journal Article
Applied Mathematics and Computation, ISSN 0096-3003, 04/2013, Volume 219, Issue 16, pp. 8584 - 8594
There are many practical situations that the chaotic signal appears in a random manner so that there are intermittent failures in the observation mechanism at... 
Additives | Filtration | Filtering | Noise | Chaos theory | Mathematical analysis | Mathematical models | Failure
Journal Article
IEEE Transactions on Control Systems Technology, ISSN 1063-6536, 01/2019, Volume 27, Issue 1, pp. 378 - 385
Journal Article
IEEE Transactions on Control Systems Technology, ISSN 1063-6536, 01/2017, Volume 25, Issue 1, pp. 366 - 373
Journal Article
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