Hybrid distance-guided adversarial network for intelligent fault diagnosis under different working conditions
Deep learning, especially transfer learning, has made a great deal of extraordinary
achievements in intelligent fault diagnosis. In practical situations, data shift problem is …
achievements in intelligent fault diagnosis. In practical situations, data shift problem is …
A novel method for imbalanced fault diagnosis of rotating machinery based on generative adversarial networks
Z Li, T Zheng, Y Wang, Z Cao, Z Guo… - IEEE transactions on …, 2020 - ieeexplore.ieee.org
In the real scenario of engineering, the failure time of rotating machinery is generally much
less than when it is in a healthy condition. Considering the cost, it is unrealistic to conduct …
less than when it is in a healthy condition. Considering the cost, it is unrealistic to conduct …
Toward small sample challenge in intelligent fault diagnosis: Attention-weighted multidepth feature fusion net with signals augmentation
T Zhang, S He, J Chen, T Pan… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Intelligent fault diagnosis of machines has yielded fruitful achievements; however, the
application in engineering scenarios is still not satisfactory. There are two reasons: 1) …
application in engineering scenarios is still not satisfactory. There are two reasons: 1) …
Data augmentation in fault diagnosis based on the Wasserstein generative adversarial network with gradient penalty
Fault detection and diagnosis in industrial process is an extremely essential part to keep
away from undesired events and ensure the safety of operators and facilities. In the last few …
away from undesired events and ensure the safety of operators and facilities. In the last few …
A multisource dense adaptation adversarial network for fault diagnosis of machinery
Deep learning theory has made great progress in the field of intelligent fault diagnosis, and
the development of domain adaptation has greatly promoted fault diagnosis under polytropic …
the development of domain adaptation has greatly promoted fault diagnosis under polytropic …
A modified generative adversarial network for fault diagnosis in high-speed train components with imbalanced and heterogeneous monitoring data
Data-driven methods are widely considered for fault diagnosis in complex systems.
However, in practice the between-class imbalance due to limited faulty samples may …
However, in practice the between-class imbalance due to limited faulty samples may …
A new generative adversarial network based imbalanced fault diagnosis method
In the field of mechanical fault diagnosis, most of the collected signals are normal signals,
leading to data imbalance and reduction of fault diagnosis performance. To address the …
leading to data imbalance and reduction of fault diagnosis performance. To address the …
Method to enhance deep learning fault diagnosis by generating adversarial samples
J Cao, J Ma, D Huang, P Yu, J Wang, K Zheng - Applied Soft Computing, 2022 - Elsevier
Modern industrial fields utilize complex mechanical equipment and machinery, which are
closely linked, and equipment faults are difficult to express. Therefore, fault diagnosis is …
closely linked, and equipment faults are difficult to express. Therefore, fault diagnosis is …
Multiscale Margin Disparity Adversarial Network Transfer Learning for Fault Diagnosis
K Sun, Z Huang, H Mao, A Yin… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Due to the limitation in obtaining sample data in the real world, domain adaption transfer
learning (DAT) has been a research focus in fault diagnosis. However, the existing DAT …
learning (DAT) has been a research focus in fault diagnosis. However, the existing DAT …
[HTML][HTML] Auxiliary generative mutual adversarial networks for class-imbalanced fault diagnosis under small samples
LI Ranran, LI Shunming, XU Kun, Z Mengjie… - Chinese Journal of …, 2023 - Elsevier
The effect of intelligent fault diagnosis of mechanical equipment based on data-driven is
often premised on big data and class-balance. However, due to the limitation of working …
often premised on big data and class-balance. However, due to the limitation of working …