Co-attention learning cross time and frequency domains for fault diagnosis
Rolling machinery is ubiquitous in power transmission and transformation equipment, but it suffers from severe faults during long-term running. Automatic fault diagnosis plays an important role in the production safety of power equipment. This paper proposes a novel cross-domain co-attention network...
Main Authors: | Ping Luo, Xinsheng Zhang, Ran Meng |
---|---|
Format: | Article |
Language: | English |
Published: |
KeAi Communications Co. Ltd.
2023-01-01
|
Series: | Cognitive Robotics |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S2667241323000095 |
Similar Items
-
Real-Time Motor Fault Diagnosis Based on TCN and Attention
by: Hui Zhang, et al.
Published: (2022-03-01) -
A Multi-Scale Attention Mechanism Based Domain Adversarial Neural Network Strategy for Bearing Fault Diagnosis
by: Quanling Zhang, et al.
Published: (2023-04-01) -
A Study on the Fault Location of Secondary Equipment in Smart Substation Based on the Graph Attention Network
by: Xian-Ming Xiang, et al.
Published: (2023-11-01) -
Mechanical and electrical equipment fault diagnosis based on dual attention mechanism and S-BiGAN
by: JIAO Xiaoxuan, et al.
Published: (2023-10-01) -
Time-Frequency Multi-Domain 1D Convolutional Neural Network with Channel-Spatial Attention for Noise-Robust Bearing Fault Diagnosis
by: Yejin Kim, et al.
Published: (2023-11-01)