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Development of intelligent fault-tolerant control systems with machine learning, deep learning, and transfer learning algorithms: a review
Abstract Intelligent Fault-Tolerant Control (IFTC) refers to the applications of machine
learning algorithms for fault diagnosis and design of Fault-Tolerant Control (FTC). The …
learning algorithms for fault diagnosis and design of Fault-Tolerant Control (FTC). The …
Challenges and opportunities of deep learning models for machinery fault detection and diagnosis: A review
In the age of industry 4.0, deep learning has attracted increasing interest for various
research applications. In recent years, deep learning models have been extensively …
research applications. In recent years, deep learning models have been extensively …
[HTML][HTML] A framework for data-driven digital twins of smart manufacturing systems
Adoption of digital twins in smart factories, that model real statuses of manufacturing systems
through simulation with real time actualization, are manifested in the form of increased …
through simulation with real time actualization, are manifested in the form of increased …
[HTML][HTML] Deep Learning approaches for visual faults diagnosis of photovoltaic systems: State-of-the-art review
PV systems are prone to external environmental conditions that affect PV system operations.
Visual inspection of the impacts of faults on PV system is considered a better practice rather …
Visual inspection of the impacts of faults on PV system is considered a better practice rather …
An analysis of process fault diagnosis methods from safety perspectives
Industry 4.0 provides substantial opportunities to ensure a safer environment through online
monitoring, early detection of faults, and preventing the faults to failures transitions. Decision …
monitoring, early detection of faults, and preventing the faults to failures transitions. Decision …
Adversarial autoencoder based feature learning for fault detection in industrial processes
Deep learning has recently emerged as a promising method for nonlinear process
monitoring. However, ensuring that the features from process variables have representative …
monitoring. However, ensuring that the features from process variables have representative …
Fault detection in Tennessee Eastman process with temporal deep learning models
Automated early process fault detection and prediction remains a challenging problem in
industrial processes. Traditionally it has been done by multivariate statistical analysis of …
industrial processes. Traditionally it has been done by multivariate statistical analysis of …
A new unsupervised data mining method based on the stacked autoencoder for chemical process fault diagnosis
S Zheng, J Zhao - Computers & Chemical Engineering, 2020 - Elsevier
Process monitoring plays an important role in chemical process safety management, and
fault diagnosis is a vital step of process monitoring. Among fault diagnosis researches …
fault diagnosis is a vital step of process monitoring. Among fault diagnosis researches …
[HTML][HTML] Updating digital twins: Methodology for data accuracy quality control using machine learning techniques
Abstract The Digital Twin (DT) constitutes an integration between cyber and physical spaces
and has recently become a popular concept in smart manufacturing and Industry 4.0. The …
and has recently become a popular concept in smart manufacturing and Industry 4.0. The …
An adaptive fault detection and root-cause analysis scheme for complex industrial processes using moving window KPCA and information geometric causal inference
In recent years, fault detection and diagnosis for industrial processes have been rapidly
developed to minimize costs and maximize efficiency by taking advantages of cheap …
developed to minimize costs and maximize efficiency by taking advantages of cheap …