A review of the application of deep learning in intelligent fault diagnosis of rotating machinery
Z Zhu, Y Lei, G Qi, Y Chai, N Mazur, Y An, X Huang - Measurement, 2023 - Elsevier
With the rapid development of industry, fault diagnosis plays a more and more important role
in maintaining the health of equipment and ensuring the safe operation of equipment. Due to …
in maintaining the health of equipment and ensuring the safe operation of equipment. Due to …
Rotating machinery fault diagnosis under time-varying speeds: A review
D Liu, L Cui, H Wang - IEEE Sensors Journal, 2023 - ieeexplore.ieee.org
Rotating machinery often works under time-varying speeds, and nonstationary conditions
and harsh environments make its key parts, such as rolling bearings and gears, prone to …
and harsh environments make its key parts, such as rolling bearings and gears, prone to …
Class-aware adversarial multiwavelet convolutional neural network for cross-domain fault diagnosis
Incomplete feature extraction and underutilization of unlabeled target data exist in the actual
situation of rotating machinery fault diagnosis. To this end, a class-aware adversarial …
situation of rotating machinery fault diagnosis. To this end, a class-aware adversarial …
Task-sequencing meta learning for intelligent few-shot fault diagnosis with limited data
Recently, deep learning-based intelligent fault diagnosis methods have been developed
rapidly, which rely on massive data to train the diagnosis model. However, it is usually …
rapidly, which rely on massive data to train the diagnosis model. However, it is usually …
Subspace network with shared representation learning for intelligent fault diagnosis of machine under speed transient conditions with few samples
Sharp speed variation leads to a shift of sample distribution domain, which poses a
challenge for vibration-based rolling bearing fault diagnosis. Furthermore, the overfitting …
challenge for vibration-based rolling bearing fault diagnosis. Furthermore, the overfitting …
Few-shot learning under domain shift: Attentional contrastive calibrated transformer of time series for fault diagnosis under sharp speed variation
The domain shift of sample distribution caused by sharp speed variation dissatisfies the
general assumption of stationary conditions, which renders a severe challenge for a majority …
general assumption of stationary conditions, which renders a severe challenge for a majority …
Coarse-to-fine: Progressive knowledge transfer-based multitask convolutional neural network for intelligent large-scale fault diagnosis
In modern industry, large-scale fault diagnosis of complex systems is emerging and
becoming increasingly important. Most deep learning-based methods perform well on small …
becoming increasingly important. Most deep learning-based methods perform well on small …
Global contextual multiscale fusion networks for machine health state identification under noisy and imbalanced conditions
CNN-based intelligent fault diagnosis methodologies have demonstrated excellent
performance in machine health condition monitoring and safety assessment. However, the …
performance in machine health condition monitoring and safety assessment. However, the …
Fault knowledge transfer assisted ensemble method for remaining useful life prediction
Machinery remaining useful life (RUL) prediction is an important task in condition-based
maintenance. Data-driven methods have been widely studied and applied, however, almost …
maintenance. Data-driven methods have been widely studied and applied, however, almost …
Multi-channel Calibrated Transformer with Shifted Windows for few-shot fault diagnosis under sharp speed variation
In engineering practice, mechanical equipment is mainly operated under the working
conditions of sharp speed variations, which results the data distribution domain shift …
conditions of sharp speed variations, which results the data distribution domain shift …