A systematic review of deep transfer learning for machinery fault diagnosis
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 …
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 …
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 …
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 …
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 …
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
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 …
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 …
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
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 …
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
Intelligent mechanical fault diagnosis techniques have been extensively developed in recent
years. Owing to the advantage of data privacy protection, federated learning has recently …
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
This article presents a new deep transfer learning method, named structured domain
adversarial neural network (SDANN), for bearing fault diagnosis with the data collected …
adversarial neural network (SDANN), for bearing fault diagnosis with the data collected …