4.7 Article

A Comparative Study of Deep Neural Network-Aided Canonical Correlation Analysis-Based Process Monitoring and Fault Detection Methods

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Automation & Control Systems

A Just-In-Time-Learning-Aided Canonical Correlation Analysis Method for Multimode Process Monitoring and Fault Detection

Zhiwen Chen et al.

Summary: A new method for monitoring and fault detection of multimode processes is proposed in the article, integrating K-means into just-in-time learning to build local models and addressing limitations of traditional canonical correlation analysis methods in handling processes with multiple operating points. Its effectiveness is demonstrated in an industrial benchmark process, showing better fault detection rate compared to conventional methods.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2021)

Article Automation & Control Systems

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

Qingchao Jiang et al.

CONTROL ENGINEERING PRACTICE (2020)

Review Engineering, Chemical

A Review of Kernel Methods for Feature Extraction in Nonlinear Process Monitoring

Karl Ezra Pilario et al.

PROCESSES (2020)

Article Computer Science, Interdisciplinary Applications

Advances and opportunities in machine learning for process data analytics

S. Joe Qin et al.

COMPUTERS & CHEMICAL ENGINEERING (2019)

Article Engineering, Mechanical

Condition monitoring of rotating machines under time-varying conditions based on adaptive canonical variate analysis

Xiaochuan Li et al.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2019)

Article Automation & Control Systems

Learning Deep Correlated Representations for Nonlinear Process Monitoring

Qingchao Jiang et al.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2019)

Article Automation & Control Systems

A Mixture of Variational Canonical Correlation Analysis for Nonlinear and Quality-Relevant Process Monitoring

Yiqi Liu et al.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2018)

Article Automation & Control Systems

Fault Detection for Non-Gaussian Processes Using Generalized Canonical Correlation Analysis and Randomized Algorithms

Zhiwen Chen et al.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2018)

Article Automation & Control Systems

Canonical Variate Dissimilarity Analysis for Process Incipient Fault Detection

Karl Ezra Salgado Pilario et al.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2018)

Article Engineering, Electrical & Electronic

Deep PCA Based Real-Time Incipient Fault Detection and Diagnosis Methodology for Electrical Drive in High-Speed Trains

Hongtian Chen et al.

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY (2018)

Article Computer Science, Artificial Intelligence

Recent advances in convolutional neural networks

Jiuxiang Gu et al.

PATTERN RECOGNITION (2018)

Article Engineering, Electrical & Electronic

Hardware-in-the-Loop Fault Injection for Traction Control System

Xiaoyue Yang et al.

IEEE JOURNAL OF EMERGING AND SELECTED TOPICS IN POWER ELECTRONICS (2018)

Article Engineering, Chemical

Locally Weighted Canonical Correlation Analysis for Nonlinear Process Monitoring

Qingchao Jiang et al.

INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH (2018)

Article Automation & Control Systems

A Fault-Injection Strategy for Traction Drive Control Systems

Chunhua Yang et al.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2017)

Article Automation & Control Systems

Canonical correlation analysis-based fault detection methods with application to alumina evaporation process

Zhiwen Chen et al.

CONTROL ENGINEERING PRACTICE (2016)

Article Automation & Control Systems

Canonical variate analysis-based monitoring of process correlation structure using causal feature representation

Benben Jiang et al.

JOURNAL OF PROCESS CONTROL (2015)

Book Automation & Control Systems

Data-driven Design of Fault Diagnosis and Fault-tolerant Control Systems

SX Ding

DATA-DRIVEN DESIGN OF FAULT DIAGNOSIS AND FAULT-TOLERANT CONTROL SYSTEMS (2014)

Article Automation & Control Systems

Nonlinear Dynamic Process Monitoring Using Canonical Variate Analysis and Kernel Density Estimations

Pabara-Ebiere Patricia Odiowei et al.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2010)

Article Engineering, Chemical

Fault detection using canonical variate analysis

BC Juricek et al.

INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH (2004)

Article Automation & Control Systems

Fault detection in industrial processes using canonical variate analysis and dynamic principal component analysis

EL Russell et al.

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS (2000)