Related references
Note: Only part of the references are listed.A Novel Dynamic Weight Principal Component Analysis Method and Hierarchical Monitoring Strategy for Process Fault Detection and Diagnosis
Yang Tao et al.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2020)
Advances and opportunities in machine learning for process data analytics
S. Joe Qin et al.
COMPUTERS & CHEMICAL ENGINEERING (2019)
Simultaneous Static and Dynamic Analysis for Fine-Scale Identification of Process Operation Statuses
Shumei Zhang et al.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2019)
Category-Based Deep CCA for Fine-Grained Venue Discovery From Multimodal Data
Yi Yu et al.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2019)
Learning Deep Correlated Representations for Nonlinear Process Monitoring
Qingchao Jiang et al.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2019)
A Mixture of Variational Canonical Correlation Analysis for Nonlinear and Quality-Relevant Process Monitoring
Yiqi Liu et al.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2018)
Fault Detection for Non-Gaussian Processes Using Generalized Canonical Correlation Analysis and Randomized Algorithms
Zhiwen Chen et al.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2018)
Canonical Variate Dissimilarity Analysis for Process Incipient Fault Detection
Karl Ezra Salgado Pilario et al.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2018)
Fault detection of process correlation structure using canonical variate analysis-based correlation features
Benben Jiang et al.
JOURNAL OF PROCESS CONTROL (2017)
Data Mining and Analytics in the Process Industry: The Role of Machine Learning
Zhiqiang Ge et al.
IEEE ACCESS (2017)
Bridging data-driven and model-based approaches for process fault diagnosis and health monitoring: A review of researches and future challenges
Khaoula Tidriri et al.
ANNUAL REVIEWS IN CONTROL (2016)
Canonical correlation analysis-based fault detection methods with application to alumina evaporation process
Zhiwen Chen et al.
CONTROL ENGINEERING PRACTICE (2016)
Fault Diagnosis Method of Joint Fisher Discriminant Analysis Based on the Local and Global Manifold Learning and Its Kernel Version
Jian Feng et al.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING (2016)
Semisupervised Kernel Learning for FDA Model and its Application for Fault Classification in Industrial Processes
Zhiqiang Ge et al.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2016)
A combined canonical variate analysis and Fisher discriminant analysis (CVA-FDA) approach for fault diagnosis
Benben Jiang et al.
COMPUTERS & CHEMICAL ENGINEERING (2015)
A Survey of Fault Diagnosis and Fault-Tolerant Techniques-Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches
Zhiwei Gao et al.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2015)
Motor Bearing Fault Diagnosis Using Trace Ratio Linear Discriminant Analysis
Xiaohang Jin et al.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2014)
A Review on Basic Data-Driven Approaches for Industrial Process Monitoring
Shen Yin et al.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2014)
A PLS-Based Statistical Approach for Fault Detection and Isolation of Robotic Manipulators
Riccardo Muradore et al.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2012)
Nonlinear Dynamic Process Monitoring Using Canonical Variate Analysis and Kernel Density Estimations
Pabara-Ebiere Patricia Odiowei et al.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2010)
Improved Fault Diagnosis Using Dynamic Kernel Scatter-Difference-Based Discriminant Analysis
C. Sumana et al.
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH (2010)
Reducing the dimensionality of data with neural networks
G. E. Hinton et al.
SCIENCE (2006)
A fast learning algorithm for deep belief nets
Geoffrey E. Hinton et al.
NEURAL COMPUTATION (2006)
A review of process fault detection and diagnosis Part II: Quantitative model and search strategies
V Venkatasubramanian et al.
COMPUTERS & CHEMICAL ENGINEERING (2003)
A review of process fault detection and diagnosis Part III: Process history based methods
V Venkatasubramanian et al.
COMPUTERS & CHEMICAL ENGINEERING (2003)
Share Paper
