A systematic review of deep transfer learning for machinery fault diagnosis

C Li, S Zhang, Y Qin, E Estupinan - Neurocomputing, 2020 - Elsevier
With the popularization of the intelligent manufacturing, much attention has been paid in
such intelligent computing methods as deep learning ones for machinery fault diagnosis …

Application of recurrent neural network to mechanical fault diagnosis: A review

J Zhu, Q Jiang, Y Shen, C Qian, F Xu, Q Zhu - Journal of Mechanical …, 2022 - Springer
With the development of intelligent manufacturing and automation, the precision and
complexity of mechanical equipment are increasing, which leads to a higher requirement for …

Conditional GAN and 2-D CNN for bearing fault diagnosis with small samples

J Yang, J Liu, J Xie, C Wang… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
The rolling bearing is the key component of rotating machinery, and it is also a failure–prone
component. The intelligent fault diagnosis method has been widely used to accurately …

Prediction and reconstruction of ocean wave heights based on bathymetric data using LSTM neural networks

C Jörges, C Berkenbrink, B Stumpe - Ocean Engineering, 2021 - Elsevier
Since climate change impacts threaten the coastal regions of the North Sea, consistent sea
state time series are essential for building coastal protection or offshore structures. Vast …

Partial transfer learning of multidiscriminator deep weighted adversarial network in cross-machine fault diagnosis

Z Wang, J Cui, W Cai, Y Li - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Deep transfer learning provides a feasible fault diagnosis method for intelligent mechanical
systems. However, this method usually assumes that the source domain and the target …

Fault detection of the harmonic reducer based on CNN-LSTM with a novel denoising algorithm

Z Zhi, L Liu, D Liu, C Hu - IEEE Sensors Journal, 2021 - ieeexplore.ieee.org
The harmonic reducer is a key component of the industrial robot. Its reliability has significant
influence on the consecutive operation of the industrial robot. However, its failure rate is high …

A novel bearing fault classification method based on XGBoost: The fusion of deep learning-based features and empirical features

J Xie, Z Li, Z Zhou, S Liu - IEEE Transactions on Instrumentation …, 2020 - ieeexplore.ieee.org
The key to intelligent fault diagnosis is to find relevant characteristics with the capability of
representing different types of faults. However, the engineering problem is that a few simple …

LEFE-Net: A lightweight efficient feature extraction network with strong robustness for bearing fault diagnosis

H Fang, J Deng, B Zhao, Y Shi, J Zhou… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
High precision and fast fault diagnosis is an important guarantee for the safe and reliable
operation of machinery. In recent years, due to the strong recognition ability, data-driven …

A federated transfer learning method with low-quality knowledge filtering and dynamic model aggregation for rolling bearing fault diagnosis

R Wang, F Yan, L Yu, C Shen, X Hu, J Chen - Mechanical Systems and …, 2023 - Elsevier
Intelligent mechanical fault diagnosis techniques have been extensively developed in recent
years. Owing to the advantage of data privacy protection, federated learning has recently …

A new structured domain adversarial neural network for transfer fault diagnosis of rolling bearings under different working conditions

W Mao, Y Liu, L Ding, A Safian… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
This article presents a new deep transfer learning method, named structured domain
adversarial neural network (SDANN), for bearing fault diagnosis with the data collected …