An integrated multitasking intelligent bearing fault diagnosis scheme based on representation learning under imbalanced sample condition
Accurate bearing fault diagnosis is of great significance of the safety and reliability of rotary
mechanical system. In practice, the sample proportion between faulty data and healthy data …
mechanical system. In practice, the sample proportion between faulty data and healthy data …
An effective fault diagnosis approach for bearing using stacked de-noising auto-encoder with structure adaptive adjustment
L Chen, Y Ma, H Hu, US Khan - Measurement, 2023 - Elsevier
Fault diagnosis of bearing plays an important role in maintaining the stable operation of
rotating equipment. However, the existing approaches are not effective enough in multi …
rotating equipment. However, the existing approaches are not effective enough in multi …
Auto-embedding transformer for interpretable few-shot fault diagnosis of rolling bearings
G Wang, D Liu, L Cui - IEEE Transactions on Reliability, 2023 - ieeexplore.ieee.org
Deep-learning-based intelligent diagnosis is a popular method to ensure the safe operation
of rolling bearings. However, practical diagnostic tasks are often subject to a lack of labeled …
of rolling bearings. However, practical diagnostic tasks are often subject to a lack of labeled …
Meta-learning for few-shot bearing fault diagnosis under complex working conditions
Deep learning-based bearing fault diagnosis has been systematically studied in recent
years. However, the success of most of these methods relies heavily on massive labeled …
years. However, the success of most of these methods relies heavily on massive labeled …
Few-shot bearing fault diagnosis based on model-agnostic meta-learning
The rapid development of artificial intelligence and deep learning has provided many
opportunities to further enhance the safety, stability, and accuracy of industrial cyber …
opportunities to further enhance the safety, stability, and accuracy of industrial cyber …
Deep dynamic adaptive transfer network for rolling bearing fault diagnosis with considering cross-machine instance
The research of intelligent fault diagnosis method has made great progress. The
prerequisite for the effectiveness of most intelligent diagnosis models is to have abundant …
prerequisite for the effectiveness of most intelligent diagnosis models is to have abundant …
An intelligent fault diagnosis method of small sample bearing based on improved auxiliary classification generative adversarial network
Z Meng, Q Li, D Sun, W Cao, F Fan - IEEE Sensors Journal, 2022 - ieeexplore.ieee.org
Intelligent diagnosis is one of the key points of research in the field of bearing fault
diagnosis. As a representative unsupervised data expansion method, generative adversarial …
diagnosis. As a representative unsupervised data expansion method, generative adversarial …
Supervised contrastive learning-based domain adaptation network for intelligent unsupervised fault diagnosis of rolling bearing
Fault diagnosis of rolling bearing is essential to guarantee production efficiency and avoid
catastrophic accidents. Domain adaptation is emerging as a critical technology for the …
catastrophic accidents. Domain adaptation is emerging as a critical technology for the …
Bearing fault diagnosis under various conditions using an incremental learning-based multi-task shared classifier
P Wang, H Xiong, H He - Knowledge-based systems, 2023 - Elsevier
Rolling bearings are susceptible to failure because of their complex and severe working
environments. Deep learning-driven intelligent fault diagnosis methods have been widely …
environments. Deep learning-driven intelligent fault diagnosis methods have been widely …
A novel method based on meta-learning for bearing fault diagnosis with small sample learning under different working conditions
H Su, L Xiang, A Hu, Y Xu, X Yang - Mechanical Systems and Signal …, 2022 - Elsevier
Recently, intelligent fault diagnosis has made great achievements, which has aroused
growing interests in the field of bearing fault diagnosis due to its strong feature learning …
growing interests in the field of bearing fault diagnosis due to its strong feature learning …