IDSN: A one-stage interpretable and differentiable STFT domain adaptation network for traction motor of high-speed trains cross-machine diagnosis
C He, H Shi, J Li - Mechanical Systems and Signal Processing, 2023 - Elsevier
A surge of transfer fault diagnosis techniques has been proposed to guarantee the safe
operation of traction motor systems. However, existing efforts highly depend on the …
operation of traction motor systems. However, existing efforts highly depend on the …
An adaptive multisensor fault diagnosis method for high-speed train traction converters
H Dong, F Chen, Z Wang, L Jia, Y Qin… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Traction converters are safety-critical parts of traction drive systems on high-speed trains.
Considering the complicated interconnections in the traction converter, it is imperative to …
Considering the complicated interconnections in the traction converter, it is imperative to …
Semisupervised subdomain adaptation graph convolutional network for fault transfer diagnosis of rotating machinery under time-varying speeds
P Liang, L Xu, H Shuai, X Yuan… - IEEE/ASME …, 2023 - ieeexplore.ieee.org
The deep learning-based fault diagnosis approaches have shown great advantages in
ensuring rotating machinery (RM) work normally and safely. However, in real industrial …
ensuring rotating machinery (RM) work normally and safely. However, in real industrial …
A hybrid generalization network for intelligent fault diagnosis of rotating machinery under unseen working conditions
The data-driven methods in machinery fault diagnosis have become increasingly popular in
the past two decades. However, the wide applications of this scheme are generally …
the past two decades. However, the wide applications of this scheme are generally …
A digital twin-driven approach for partial domain fault diagnosis of rotating machinery
Artificial intelligence (AI)-driven fault diagnosis methods are crucial for ensuring rotating
machinery's safety and effective operation. The success of most current methods relies on …
machinery's safety and effective operation. The success of most current methods relies on …
A new deep transfer learning network based on convolutional auto-encoder for mechanical fault diagnosis
Deep learning has gained a great achievement in the intelligent fault diagnosis of rotating
machineries. However, the labeled data is scarce in actual engineering and the marginal …
machineries. However, the labeled data is scarce in actual engineering and the marginal …
Cross‐machine intelligent fault diagnosis of gearbox based on deep learning and parameter transfer
With the rapid development of artificial intelligence technologies, data‐driven methods have
significantly contributed to the intelligent monitoring and diagnosis of mechanical systems …
significantly contributed to the intelligent monitoring and diagnosis of mechanical systems …
Combining the theoretical bound and deep adversarial network for machinery open-set diagnosis transfer
Recently, deep transfer learning-based intelligent machine diagnosis has been well
investigated, and the source and the target domain are commonly assumed to share the …
investigated, and the source and the target domain are commonly assumed to share the …
Incipient fault diagnosis for high-speed train traction systems via stacked generalization
Z Mao, M Xia, B Jiang, D Xu… - IEEE transactions on …, 2020 - ieeexplore.ieee.org
Diagnosing the fault as early as possible is significant to guarantee the safety and reliability
of the high-speed train. Incipient fault always makes the monitored signals deviate from their …
of the high-speed train. Incipient fault always makes the monitored signals deviate from their …
Deep discriminative transfer learning network for cross-machine fault diagnosis
Many domain adaptation methods have been presented to deal with the distribution
alignment and knowledge transfer between the target domain and the source domain …
alignment and knowledge transfer between the target domain and the source domain …