[HTML][HTML] Semi-supervised learning for industrial fault detection and diagnosis: A systemic review
JM Ramírez-Sanz, JA Maestro-Prieto… - ISA transactions, 2023 - Elsevier
Abstract The automation of Fault Detection and Diagnosis (FDD) is a central task for many
industries today. A myriad of methods are in use, although the most recent leading …
industries today. A myriad of methods are in use, although the most recent leading …
Deep learning for time-series prediction in IIoT: progress, challenges, and prospects
Time-series prediction plays a crucial role in the Industrial Internet of Things (IIoT) to enable
intelligent process control, analysis, and management, such as complex equipment …
intelligent process control, analysis, and management, such as complex equipment …
A CNN-BiLSTM model with attention mechanism for earthquake prediction
Earthquakes, as natural phenomena, have consistently caused damage and loss of human
life throughout history. Earthquake prediction is an essential aspect of any society's plans …
life throughout history. Earthquake prediction is an essential aspect of any society's plans …
A novel domain generalization network with multidomain specific auxiliary classifiers for machinery fault diagnosis under unseen working conditions
The domain adaptation-based intelligent diagnosis approaches have achieved promising
performance on diagnosis tasks under different working conditions. However, these …
performance on diagnosis tasks under different working conditions. However, these …
Spatial graph convolutional neural network via structured subdomain adaptation and domain adversarial learning for bearing fault diagnosis
Unsupervised domain adaptation (UDA) has shown remarkable results in fault diagnosis
under changing working conditions in recent years. However, most UDA methods do not …
under changing working conditions in recent years. However, most UDA methods do not …
Latest innovations in the field of condition-based maintenance of rotatory machinery: A review
A Kumar, CP Gandhi, H Tang, W Sun… - Measurement Science …, 2023 - iopscience.iop.org
Health monitoring in rotatory machinery is a process of develo** a mechanism to
determine its state of deterioration. It involves analysing the presence of damage, locating …
determine its state of deterioration. It involves analysing the presence of damage, locating …
A graph neural network-based data cleaning method to prevent intelligent fault diagnosis from data contamination
The success of deep learning (DL) based-mechanical fault diagnosis hinges on the high
quality of training data. However, it is difficult to acquire high-quality mechanical monitoring …
quality of training data. However, it is difficult to acquire high-quality mechanical monitoring …
[HTML][HTML] On the effects of data normalization for domain adaptation on EEG data
Abstract In Machine Learning (ML), a well-known problem is the Dataset Shift problem
where the data in the training and test sets can follow different probability distributions …
where the data in the training and test sets can follow different probability distributions …
Federated contrastive prototype learning: An efficient collaborative fault diagnosis method with data privacy
Data-driven fault diagnosis approaches have attracted considerable attention in the past few
years, and promising diagnostic performance has been achieved with sufficient monitoring …
years, and promising diagnostic performance has been achieved with sufficient monitoring …
Triplet adversarial Learning-driven graph architecture search network augmented with Probsparse-attention mechanism for fault diagnosis under Few-shot & Domain …
Y Chang, J Chen, W Zheng, S He, E Xu - Mechanical Systems and Signal …, 2023 - Elsevier
The consistent probability distribution between training & testing data is one of the
prerequisites for valid intelligent diagnosis models. Nevertheless, the ineluctable distribution …
prerequisites for valid intelligent diagnosis models. Nevertheless, the ineluctable distribution …