One step forward for smart chemical process fault detection and diagnosis
X Bi, R Qin, D Wu, S Zheng, J Zhao - Computers & Chemical Engineering, 2022 - Elsevier
Process fault detection and diagnosis (FDD) is an essential tool to ensure safe production in
chemical industries. After decades of development, despite the promising performance of …
chemical industries. After decades of development, despite the promising performance of …
Transforming data into actionable knowledge for fault detection, diagnosis and prognosis in urban wastewater systems with AI techniques: A mini-review
Recent advances in artificial intelligence (AI) and data analytics (DA) could provide
opportunities for the fault management and the decision-making of the urban wastewater …
opportunities for the fault management and the decision-making of the urban wastewater …
Machinery fault diagnosis based on domain adaptation to bridge the gap between simulation and measured signals
In intelligent fault diagnosis, the success of artificial intelligence (AI) models is highly
dependent on labeled training samples, which may not be obtained in real-world …
dependent on labeled training samples, which may not be obtained in real-world …
Domain adaptation network base on contrastive learning for bearings fault diagnosis under variable working conditions
Y An, K Zhang, Y Chai, Q Liu, X Huang - Expert Systems with Applications, 2023 - Elsevier
Unsupervised domain adaptation (UDA)-based methods have made great progress in
bearing fault diagnosis under variable working conditions. However, most existing UDA …
bearing fault diagnosis under variable working conditions. However, most existing UDA …
Fault detection and diagnosis of the air handling unit via combining the feature sparse representation based dynamic SFA and the LSTM network
H Zhang, C Li, Q Wei, Y Zhang - Energy and buildings, 2022 - Elsevier
In recent years, slow feature analysis (SFA) has been successfully employed to deal with the
air handling unit (AHU) system's time-varying dynamic properties. However, since the …
air handling unit (AHU) system's time-varying dynamic properties. However, since the …
Parameter sharing adversarial domain adaptation networks for fault transfer diagnosis of planetary gearboxes
The domain adaptation (DA) model, aiming to solve the task of unlabeled or less-labeled
target domain fault classification through the training of labeled source domain fault data, is …
target domain fault classification through the training of labeled source domain fault data, is …
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 …
[HTML][HTML] A novel fault detection and diagnosis approach based on orthogonal autoencoders
In recent years, there have been studies focusing on the use of different types of
autoencoders (AEs) for monitoring complex nonlinear data coming from industrial and …
autoencoders (AEs) for monitoring complex nonlinear data coming from industrial and …
A comparative study on long short-term memory and gated recurrent unit neural networks in fault diagnosis for chemical processes using visualization
Recurrent neural networks (RNNs), particularly those with gated units, such as long short-
term memory (LSTM) and gated recurrent unit (GRU), have demonstrated clear superiority in …
term memory (LSTM) and gated recurrent unit (GRU), have demonstrated clear superiority in …
Pruning graph convolutional network-based feature learning for fault diagnosis of industrial processes
Y Zhang, J Yu - Journal of Process Control, 2022 - Elsevier
In recent years, deep learning has been widely applied in process fault diagnosis due to its
powerful feature extraction ability. A predominant property of these fault diagnosis models is …
powerful feature extraction ability. A predominant property of these fault diagnosis models is …