Deep learning techniques in intelligent fault diagnosis and prognosis for industrial systems: a review
S Qiu, X Cui, Z Ping, N Shan, Z Li, X Bao, X Xu - Sensors, 2023 - mdpi.com
Fault diagnosis and prognosis (FDP) tries to recognize and locate the faults from the
captured sensory data, and also predict their failures in advance, which can greatly help to …
captured sensory data, and also predict their failures in advance, which can greatly help to …
A systematic review on imbalanced learning methods in intelligent fault diagnosis
The theoretical developments of data-driven fault diagnosis methods have yielded fruitful
achievements and significantly benefited industry practices. However, most methods are …
achievements and significantly benefited industry practices. However, most methods are …
Dual-threshold attention-guided GAN and limited infrared thermal images for rotating machinery fault diagnosis under speed fluctuation
End-to-end intelligent diagnosis of rotating machinery under speed fluctuation and limited
samples is challenging in industrial practice. The existing limited samples methods usually …
samples is challenging in industrial practice. The existing limited samples methods usually …
Adaptive variational autoencoding generative adversarial networks for rolling bearing fault diagnosis
X Wang, H Jiang, Z Wu, Q Yang - Advanced Engineering Informatics, 2023 - Elsevier
The fault diagnosis of rolling bearings with imbalanced data has always been a particularly
challenging problem. With data augmentation methods to complement the imbalanced …
challenging problem. With data augmentation methods to complement the imbalanced …
Fine-tuning transfer learning based on DCGAN integrated with self-attention and spectral normalization for bearing fault diagnosis
In the current big-data context of Industry 4.0, insufficient training data has become a major
bottleneck in developing data-driven diagnosis approaches, restricting the accuracy of deep …
bottleneck in developing data-driven diagnosis approaches, restricting the accuracy of deep …
Review of resampling techniques for the treatment of imbalanced industrial data classification in equipment condition monitoring
Y Yuan, J Wei, H Huang, W Jiao, J Wang… - … Applications of Artificial …, 2023 - Elsevier
In an actual industrial scenario, machines typically operate normally for the majority of the
time, with malfunctions occurring only occasionally. As a result, there is very little recorded …
time, with malfunctions occurring only occasionally. As a result, there is very little recorded …
Prior knowledge-embedded meta-transfer learning for few-shot fault diagnosis under variable operating conditions
In recent years, intelligent fault diagnosis based on deep learning has achieved vigorous
development thanks to its powerful feature representation ability. However, scarcity of high …
development thanks to its powerful feature representation ability. However, scarcity of high …
Imbalance fault diagnosis under long-tailed distribution: Challenges, solutions and prospects
Intelligent fault diagnosis based on deep learning has yielded remarkable progress for its
strong feature representation capability in recent years. Nevertheless, in engineering …
strong feature representation capability in recent years. Nevertheless, in engineering …
Few-shot GAN: Improving the performance of intelligent fault diagnosis in severe data imbalance
In severe data imbalance scenarios, fault samples are generally scarce, challenging the
health management of industrial machinery significantly. Generative adversarial network …
health management of industrial machinery significantly. Generative adversarial network …
Detection of compound faults in ball bearings using multiscale-SinGAN, heat transfer search optimization, and extreme learning machine
Intelligent fault diagnosis gives timely information about the condition of mechanical
components. Since rolling element bearings are often used as rotating equipment parts, it is …
components. Since rolling element bearings are often used as rotating equipment parts, it is …