A review on the progress, challenges and prospects in the modeling, simulation, control and diagnosis of thermodynamic systems

D Zhou, D Huang - Advanced Engineering Informatics, 2024‏ - Elsevier
Thermodynamic systems play an inestimable role in engineering applications. With the
rising demands for information and automation in operation and maintenance in …

Review of resampling techniques for the treatment of imbalanced industrial data classification in equipment condition monitoring

Y Yuan, J Wei, H Huang, W Jiao, J Wang… - … Applications of Artificial …, 2023‏ - Elsevier
In an actual industrial scenario, machines typically operate normally for the majority of the
time, with malfunctions occurring only occasionally. As a result, there is very little recorded …

Triplet attention-enhanced residual tree-inspired decision network: A hierarchical fault diagnosis model for unbalanced bearing datasets

L Cui, Z Dong, H Xu, D Zhao - Advanced Engineering Informatics, 2024‏ - Elsevier
In fault classification tasks, deep neural networks (DNNs) have remarkable recognition
performance. Nevertheless, the classification decision processes of DNNs lack hierarchical …

Channel attention & temporal attention based temporal convolutional network: A dual attention framework for remaining useful life prediction of the aircraft engines

L Lin, J Wu, S Fu, S Zhang, C Tong, L Zu - Advanced Engineering …, 2024‏ - Elsevier
The health of the aircraft engines is of great concern. And it is a key task to predict the
remaining useful life (RUL) of the aircraft engines accurately. However, there are still …

Recognition and optimisation method of impact deformation patterns based on point cloud and deep clustering: Applied to thin-walled tubes

C Yang, Z Li, P Xu, H Huang - Journal of Industrial Information Integration, 2024‏ - Elsevier
The recognition and clustering of deformation modes are key to constructing impact
deformation constraints for thin-walled structures. This paper transforms the clustering and …

An adaptive domain adaptation method for rolling bearings' fault diagnosis fusing deep convolution and self-attention networks

X Yu, Y Wang, Z Liang, H Shao, K Yu… - IEEE Transactions on …, 2023‏ - ieeexplore.ieee.org
Intelligent fault diagnosis methods based on deep learning have attracted significant
attention in recent years. However, it still faces many challenges, including complex and …

Feature-level SMOTE: Augmenting fault samples in learnable feature space for imbalanced fault diagnosis of gas turbines

D Liu, S Zhong, L Lin, M Zhao, X Fu, X Liu - Expert Systems with …, 2024‏ - Elsevier
A challenge in gas turbine fault diagnosis is that labeled fault samples are relatively rare and
much fewer than normal samples. Conventional data augmentation techniques generate …

Fault diagnosis study of hydraulic pump based on improved symplectic geometry reconstruction data enhancement method

S Liu, J Yin, M Hao, P Liang, Y Zhang, C Ai… - Advanced Engineering …, 2024‏ - Elsevier
Aiming at the problem of poor consistency between the enhanced samples and the original
samples in the current data enhancement methods. In this paper, we propose a data …

Deep attention SMOTE: Data augmentation with a learnable interpolation factor for imbalanced anomaly detection of gas turbines

D Liu, S Zhong, L Lin, M Zhao, X Fu, X Liu - Computers in Industry, 2023‏ - Elsevier
Anomaly detection of gas turbines faces the significant challenges of data imbalance and
inter-class overlap. In this paper, we develop a novel data augmentation method, namely …

Digital financial asset price fluctuation forecasting in digital economy era using blockchain information: A reconstructed dynamic-bound Levenberg–Marquardt neural …

D Shang, Z Yan, L Zhang, Z Cui - Expert Systems with Applications, 2023‏ - Elsevier
Digital financial assets such as cryptocurrency are playing an increasingly crucial role in the
digital economy era. Cryptocurrency is characterized by significant volatility and asset price …