4.6 Article

Sparsity and manifold regularized convolutional auto-encoders-based feature learning for fault detection of multivariate processes

Related references

Note: Only part of the references are listed.
Article Computer Science, Interdisciplinary Applications

Fault detection and diagnosis in process system using artificial intelligence-based cognitive technique

Rajeevan Arunthavanathan et al.

COMPUTERS & CHEMICAL ENGINEERING (2020)

Article Automation & Control Systems

Data-driven individual-joint learning framework for nonlinear process monitoring

Qingchao Jiang et al.

CONTROL ENGINEERING PRACTICE (2020)

Article Automation & Control Systems

One-dimensional convolutional auto-encoder-based feature learning for fault diagnosis of multivariate processes

Shumei Chen et al.

JOURNAL OF PROCESS CONTROL (2020)

Article Computer Science, Artificial Intelligence

Dense connection and depthwise separable convolution based CNN for polarimetric SAR image classification

Ronghua Shang et al.

KNOWLEDGE-BASED SYSTEMS (2020)

Article Automation & Control Systems

Distributed dictionary learning for high-dimensional process monitoring

Keke Huang et al.

CONTROL ENGINEERING PRACTICE (2020)

Article Automation & Control Systems

Data-Driven Two-Dimensional Deep Correlated Representation Learning for Nonlinear Batch Process Monitoring

Qingchao Jiang et al.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2020)

Article Automation & Control Systems

Nonlinear fault detection for batch processes via improved chordal kernel tensor locality preserving projections

Yujie Zhou et al.

CONTROL ENGINEERING PRACTICE (2020)

Article Automation & Control Systems

One-Dimensional Residual Convolutional Autoencoder Based Feature Learning for Gearbox Fault Diagnosis

Jianbo Yu et al.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2020)

Article Engineering, Chemical

A batch-wise LSTM-encoder decoder network for batch process monitoring

Jiayang Ren et al.

CHEMICAL ENGINEERING RESEARCH & DESIGN (2020)

Article Engineering, Electrical & Electronic

Deep Depthwise Separable Convolutional Network for Change Detection in Optical Aerial Images

Ruochen Liu et al.

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING (2020)

Article Engineering, Multidisciplinary

Stacked denoising autoencoder-based feature learning for out-of-control source recognition in multivariate manufacturing process

Jianbo Yu et al.

QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL (2019)

Article Computer Science, Artificial Intelligence

Design teacher and supervised dual stacked auto-encoders for quality-relevant fault detection in industrial process

Shifu Yan et al.

APPLIED SOFT COMPUTING (2019)

Article Automation & Control Systems

Dynamic reconstruction based representation learning for multivariable process monitoring

Feiya Lv et al.

JOURNAL OF PROCESS CONTROL (2019)

Article Automation & Control Systems

Active features extracted by deep belief network for process monitoring

Jianbo Yu et al.

ISA TRANSACTIONS (2019)

Article Automation & Control Systems

Nonlinear Dynamic Soft Sensor Modeling With Supervised Long Short-Term Memory Network

Xiaofeng Yuan et al.

IEEE Transactions on Industrial Informatics (2019)

Article Computer Science, Interdisciplinary Applications

Deep convolutional neural network model based chemical process fault diagnosis

Hao Wu et al.

COMPUTERS & CHEMICAL ENGINEERING (2018)

Article Computer Science, Artificial Intelligence

1-D CNNs for structural damage detection: Verification on a structural health monitoring benchmark data

Osama Abdeljaber et al.

NEUROCOMPUTING (2018)

Article Computer Science, Information Systems

Sequential Fault Diagnosis Based on LSTM Neural Network

Haitao Zhao et al.

IEEE ACCESS (2018)

Article Engineering, Manufacturing

A Convolutional Neural Network for Fault Classification and Diagnosis in Semiconductor Manufacturing Processes

Ki Bum Lee et al.

IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING (2017)

Article Automation & Control Systems

Stacked Convolutional Denoising Auto-Encoders for Feature Representation

Bo Du et al.

IEEE TRANSACTIONS ON CYBERNETICS (2017)

Article Automation & Control Systems

Ensemble modified independent component analysis for enhanced non-Gaussian process monitoring

Chudong Tong et al.

CONTROL ENGINEERING PRACTICE (2017)

Proceedings Paper Computer Science, Artificial Intelligence

Xception: Deep Learning with Depthwise Separable Convolutions

Francois Chollet

30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017) (2017)

Article Automation & Control Systems

Process monitoring of iron-making process in a blast furnace with PCA-based methods

Bo Zhou et al.

CONTROL ENGINEERING PRACTICE (2016)

Article Automation & Control Systems

Process monitoring through manifold regularization-based GMM with global/local information

Jianbo Yu

JOURNAL OF PROCESS CONTROL (2016)

Article Computer Science, Artificial Intelligence

Recent advances on SVM based fault diagnosis and process monitoring in complicated industrial processes

Zuyu Yin et al.

NEUROCOMPUTING (2016)

Article Computer Science, Artificial Intelligence

Bivariate quality control using two-stage intelligent monitoring scheme

Ibrahim Masood et al.

EXPERT SYSTEMS WITH APPLICATIONS (2014)

Article Automation & Control Systems

Local and global principal component analysis for process monitoring

Jianbo Yu

JOURNAL OF PROCESS CONTROL (2012)

Article Mathematics, Applied

Principal manifolds and nonlinear dimensionality reduction via tangent space alignment

ZY Zhang et al.

SIAM JOURNAL ON SCIENTIFIC COMPUTING (2004)

Article Computer Science, Artificial Intelligence

Laplacian eigenmaps for dimensionality reduction and data representation

M Belkin et al.

NEURAL COMPUTATION (2003)

Article Computer Science, Artificial Intelligence

Intelligent remote monitoring and diagnosis of manufacturing processes using an integrated approach of neural networks and rough sets

TH Hou et al.

JOURNAL OF INTELLIGENT MANUFACTURING (2003)

Article Multidisciplinary Sciences

Nonlinear dimensionality reduction by locally linear embedding

ST Roweis et al.

SCIENCE (2000)