Fault Diagnosis for Mobile Robots Based on Spatial–Temporal Graph Attention Network Under Imbalanced Data
Author:
Affiliation:
1. School of Control Science and Engineering, Shandong University, Jinan, China
2. School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai, China
Funder
Jinan City’s “New Universities 20” Funded Research Leader Studio Project
Major Agricultural Applied Technological Innovation Projects of Shandong Province
Shandong Provincial Natural Science Foundation
Young Scholars Program of Shandong University, Weihai
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Instrumentation
Link
http://xplorestaging.ieee.org/ielx7/19/10012124/10273742.pdf?arnumber=10273742
Reference37 articles.
1. CNN parameter design based on fault signal analysis and its application in bearing fault diagnosis
2. Real-time crash risk prediction on arterials based on LSTM-CNN
3. Adaptive fault diagnosis method for rotating machinery with unknown faults under multiple working conditions
4. On the Integration of Self-Attention and Convolution
5. Fault Diagnosis Based on Attention Collaborative LSTM Networks for NPC Three-Level Inverters
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