Data-driven fault detection for chemical processes using autoencoder with data augmentation

Data augmentation in fault diagnosis based on the Wasserstein generative adversarial network with gradient penalty

(2019) Xin Gao et al.   NEUROCOMPUTING

Statistical Process Monitoring of the Tennessee Eastman Process Using Parallel Autoassociative Neural Networks and a Large Dataset

(2019) Seongmin Heo et al.   Processes

Parallel neural networks for improved nonlinear principal component analysis

(2019) Seongmin Heo et al.   COMPUTERS & CHEMICAL ENGINEERING

Neural component analysis for fault detection

(2018) Haitao Zhao   CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS

Automated feature learning for nonlinear process monitoring – An approach using stacked denoising autoencoder and k-nearest neighbor rule

(2018) Zehan Zhang et al.   JOURNAL OF PROCESS CONTROL

Efficient Process Monitoring via the Integrated Use of Markov Random Fields Learning and the Graphical Lasso

(2018) Changsoo Kim et al.   INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

Easy technique for emergency fenestration in non-fenestrated Fontan patients

(2017) Qiang Fan et al.   Interactive Cardiovascular and Thoracic Surgery

Nonlinear and robust statistical process monitoring based on variant autoencoders

(2016) Weiwu Yan et al.   CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS