Domain generalization for cross-domain fault diagnosis: An application-oriented perspective and a benchmark study
Most data-driven methods for fault diagnostics rely on the assumption of independently and
identically distributed data of training and testing. However, domain shift between the …
identically distributed data of training and testing. However, domain shift between the …
A review of remaining useful life prediction approaches for mechanical equipment
Y Zhang, L Fang, Z Qi, H Deng - IEEE Sensors Journal, 2023 - ieeexplore.ieee.org
The precise maintenance and scientific management of large and complex mechanical
equipment are of great significance for ensuring the safe operation of equipment and …
equipment are of great significance for ensuring the safe operation of equipment and …
Digital twin enabled domain adversarial graph networks for bearing fault diagnosis
The fault diagnosis of rolling bearings is of utmost importance in industrial applications to
ensure mechanical systems' reliability, safety, and economic viability. However …
ensure mechanical systems' reliability, safety, and economic viability. However …
A graph-guided collaborative convolutional neural network for fault diagnosis of electromechanical systems
Collaborative fault diagnosis has become a hot research topic in fault detection and
identification, greatly benefiting from emerging multisensory fusion techniques and newly …
identification, greatly benefiting from emerging multisensory fusion techniques and newly …
Global contextual feature aggregation networks with multiscale attention mechanism for mechanical fault diagnosis under non-stationary conditions
In recent years, the rapid development of convolutional neural networks (CNNs) has
significantly advanced the progress of intelligent fault diagnosis. Most currently-available …
significantly advanced the progress of intelligent fault diagnosis. Most currently-available …
IFD-MDCN: Multibranch denoising convolutional networks with improved flow direction strategy for intelligent fault diagnosis of rolling bearings under noisy conditions
Rolling bearings are the core components of rotating machinery, and their normal operation
is crucial to the entire industrial production. Most existing condition monitoring methods have …
is crucial to the entire industrial production. Most existing condition monitoring methods have …
Digital twin-driven focal modulation-based convolutional network for intelligent fault diagnosis
Rolling bearings are essential components of various rotating machinery and are critical in
ensuring safe and reliable industrial production. Deep learning techniques have …
ensuring safe and reliable industrial production. Deep learning techniques have …
Online knowledge distillation based multiscale threshold denoising networks for fault diagnosis of transmission systems
Convolutional neural networks (CNN) have developed rapidly in recent years, which has
greatly promoted the advancement of intelligent fault diagnosis. Most currently available …
greatly promoted the advancement of intelligent fault diagnosis. Most currently available …
Multi-attention-based Feature Aggregation Convolutional Networks with Dual Focal Loss for Fault Diagnosis of Rotating Machinery Under Data Imbalance Conditions
Convolutional neural network (CNN)-based intelligent fault diagnosis approaches have
showcased remarkable performance in the assessment of machine safety. The data …
showcased remarkable performance in the assessment of machine safety. The data …
A synchronization-induced cross-modal contrastive learning strategy for fault diagnosis of electromechanical systems under semi-supervised learning with current …
Q Luo, J Chen, Y Zi, J Xie - Expert Systems with Applications, 2024 - Elsevier
Electromechanical systems is widely employed in the manufacturing industry, with fault
diagnosis being critical for ensuring the reliable operation of them. Vibration signals exhibit …
diagnosis being critical for ensuring the reliable operation of them. Vibration signals exhibit …