Attention mechanism in intelligent fault diagnosis of machinery: A review of technique and application
Attention Mechanism has become very popular in the field of mechanical fault diagnosis in
recent years and has become an important technique for scholars to study and apply. The …
recent years and has become an important technique for scholars to study and apply. The …
Multi-fault diagnosis of Industrial Rotating Machines using Data-driven approach: A review of two decades of research
Industry 4.0 is an era of smart manufacturing. Manufacturing is impossible without the use of
machinery. The majority of these machines comprise rotating components and are called …
machinery. The majority of these machines comprise rotating components and are called …
Semi-supervised adversarial discriminative learning approach for intelligent fault diagnosis of wind turbine
Wind turbines play a crucial role in renewable energy generation systems and are frequently
exposed to challenging operational environments. Monitoring and diagnosing potential …
exposed to challenging operational environments. Monitoring and diagnosing potential …
Generalized MAML for few-shot cross-domain fault diagnosis of bearing driven by heterogeneous signals
Despite a few recent meta-learning studies have facilitated few-shot cross-domain fault
diagnosis of bearing, they are limited to homogenous signal analysis and have challenges …
diagnosis of bearing, they are limited to homogenous signal analysis and have challenges …
Attention-based deep meta-transfer learning for few-shot fine-grained fault diagnosis
Deep learning-based fault diagnosis methods have made tremendous progress in recent
years; however, most of these methods are coarse grained and data demanding that cannot …
years; however, most of these methods are coarse grained and data demanding that cannot …
Meta-learning as a promising approach for few-shot cross-domain fault diagnosis: Algorithms, applications, and prospects
Y Feng, J Chen, J Xie, T Zhang, H Lv, T Pan - Knowledge-Based Systems, 2022 - Elsevier
The advances of intelligent fault diagnosis in recent years show that deep learning has
strong capability of automatic feature extraction and accurate identification for fault signals …
strong capability of automatic feature extraction and accurate identification for fault signals …
Improving state-of-health estimation for lithium-ion batteries via unlabeled charging data
The state-of-health (SOH) estimation is an important and open issue in battery health
management. Most existing data driven SOH estimation methods are based on supervised …
management. Most existing data driven SOH estimation methods are based on supervised …
End to end multi-task learning with attention for multi-objective fault diagnosis under small sample
In recent years, deep learning (DL) based intelligent fault diagnosis method has been widely
applied in the field of equipment fault diagnosis. However, most of the existing methods are …
applied in the field of equipment fault diagnosis. However, most of the existing methods are …
Cross-domain fault diagnosis of bearing using improved semi-supervised meta-learning towards interference of out-of-distribution samples
The study of cross-domain semi-supervised fault diagnosis of bearings using meta-learning
technique has important practical significance. However, existing methods fail to consider …
technique has important practical significance. However, existing methods fail to consider …
Few-shot fault diagnosis of turnout switch machine based on semi-supervised weighted prototypical network
The turnout switch machine is a critical equipment of the signal system, which has a
significant influence on the safety of train. However, it is difficult to obtain a mass of labeled …
significant influence on the safety of train. However, it is difficult to obtain a mass of labeled …