Related references
Note: Only part of the references are listed.Big data driven jobs remaining time prediction in discrete manufacturing system: a deep learning-based approach
Weiguang Fang et al.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH (2020)
Detection of interferences in an additive manufacturing process: an experimental study integrating methods of feature selection and machine learning
Darko Stanisavljevic et al.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH (2020)
One-dimensional convolutional auto-encoder-based feature learning for fault diagnosis of multivariate processes
Shumei Chen et al.
JOURNAL OF PROCESS CONTROL (2020)
From data to big data in production research: the past and future trends
Yong-Hong Kuo et al.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH (2019)
An overview of current technologies and emerging trends in factory automation
Mariagrazia Dotoli et al.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH (2019)
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)
Intelligent fault diagnosis of rotating machinery based on one-dimensional convolutional neural network
Chunzhi Wu et al.
COMPUTERS IN INDUSTRY (2019)
Digital Twin for rotating machinery fault diagnosis in smart manufacturing
Jinjiang Wang et al.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH (2019)
Active features extracted by deep belief network for process monitoring
Jianbo Yu et al.
ISA TRANSACTIONS (2019)
Speech Recognition Using Deep Neural Networks: A Systematic Review
Ali Bou Nassif et al.
IEEE ACCESS (2019)
Deep convolutional neural network model based chemical process fault diagnosis
Hao Wu et al.
COMPUTERS & CHEMICAL ENGINEERING (2018)
A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method
Long Wen et al.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2018)
Deep learning for smart manufacturing: Methods and applications
Jinjiang Wang et al.
JOURNAL OF MANUFACTURING SYSTEMS (2018)
Optimal false alarm controlled support vector data description for multivariate process monitoring
Younghoon Kim et al.
JOURNAL OF PROCESS CONTROL (2018)
A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load
Wei Zhang et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2018)
1-D CNNs for structural damage detection: Verification on a structural health monitoring benchmark data
Osama Abdeljaber et al.
NEUROCOMPUTING (2018)
A Deep Belief Network-based Fault Detection Method for Nonlinear Processes
Peng Tang et al.
IFAC PAPERSONLINE (2018)
A Convolutional Neural Network for Fault Classification and Diagnosis in Semiconductor Manufacturing Processes
Ki Bum Lee et al.
IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING (2017)
Weighted time series fault diagnosis based on a stacked sparse autoencoder
Feiya Lv et al.
JOURNAL OF CHEMOMETRICS (2017)
Interpreting out-of-control signals using instance-based bayesian classifier in multivariate statistical process control
Huaming Song et al.
COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION (2017)
Structural Damage Detection in Real Time: Implementation of 1D Convolutional Neural Networks for SHM Applications
Onur Avci et al.
STRUCTURAL HEALTH MONITORING & DAMAGE DETECTION, VOL 7 (2017)
Real-Time Patient-Specific ECG Classification by 1-D Convolutional Neural Networks
Serkan Kiranyaz et al.
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING (2016)
Real-Time Motor Fault Detection by 1-D Convolutional Neural Networks
Turker Ince et al.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2016)
An efficient ICA-DW-SVDD fault detection and diagnosis method for non-Gaussian processes
Mu-Chen Chen et al.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH (2016)
Fed-batch fermentation penicillin process fault diagnosis and detection based on support vector machine
Chengming Yang et al.
NEUROCOMPUTING (2016)
Deep learning for visual understanding: A review
Yanming Guo et al.
NEUROCOMPUTING (2016)
Recent advances on SVM based fault diagnosis and process monitoring in complicated industrial processes
Zuyu Yin et al.
NEUROCOMPUTING (2016)
Monitoring and diagnosing of mean shifts in multivariate manufacturing processes using two-level selective ensemble of learning vector quantization neural networks
Wen-An Yang
JOURNAL OF INTELLIGENT MANUFACTURING (2015)
A hybrid wrapper-filter approach to detect the source(s) of out-of-control signals in multivariate manufacturing process
Shamsul Huda et al.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH (2014)
Bivariate quality control using two-stage intelligent monitoring scheme
Ibrahim Masood et al.
EXPERT SYSTEMS WITH APPLICATIONS (2014)
Independent component analysis-based non-Gaussian process monitoring with preselecting optimal components and support vector data description
Qingchao Jiang et al.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH (2014)
Multivariate process monitoring and fault identification using multiple decision tree classifiers
Shuguang He et al.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH (2013)
Survey on data-driven industrial process monitoring and diagnosis
S. Joe Qin
ANNUAL REVIEWS IN CONTROL (2012)
Local and global principal component analysis for process monitoring
Jianbo Yu
JOURNAL OF PROCESS CONTROL (2012)
Fault Detection Using Principal Components-Based Gaussian Mixture Model for Semiconductor Manufacturing Processes
Jianbo Yu
IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING (2011)
A neural network ensemble model for on-line monitoring of process mean and variance shifts in correlated processes
Bin Wu et al.
EXPERT SYSTEMS WITH APPLICATIONS (2010)
Performance monitoring of processes with multiple operating modes through multiple PLS models
Shi Jian Zhao et al.
JOURNAL OF PROCESS CONTROL (2006)
Reducing the dimensionality of data with neural networks
G. E. Hinton et al.
SCIENCE (2006)
A kernel-distance-based multivariate control chart using support vector methods
RX Sun et al.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH (2003)
Improved SPC chart pattern recognition using statistical features
A Hassan et al.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH (2003)
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)
Modelling and diagnosis of feedback-controlled processes using dynamic PCA and neural networks
DF Shi et al.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH (2003)
Independent component analysis:: algorithms and applications
A Hyvärinen et al.
NEURAL NETWORKS (2000)
Statistical monitoring and diagnosis of automatic controlled processes using dynamic PCA
F Tsung
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH (2000)
Share Paper
