Graph-Guided Higher-Order Attention Network for Industrial Rotating Machinery Intelligent Fault Diagnosis
Author:
Affiliation:
1. College of Internet of Internet of Things Engineering, Hohai University, Changzhou, China
2. Artificial Intelligence Research Center, National Innovation Institute of Defense Technology, Beijing, China
Funder
National Natural Science Foundation of China
Natural Science Foundation of Jiangsu Province of China
Changzhou Foundation of Science and Technology
Open Fund of State Key Laboratory of Robotics
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Computer Science Applications,Information Systems,Control and Systems Engineering
Link
http://xplorestaging.ieee.org/ielx7/9424/10411967/10065538.pdf?arnumber=10065538
Reference38 articles.
1. Sparse Representation Convolutional Autoencoder for Feature Learning of Vibration Signals and Its Applications in Machinery Fault Diagnosis
2. Generalization of deep neural network for bearing fault diagnosis under different working conditions using multiple kernel method
3. A Squeezed Modulation Signal Bispectrum Method for Motor Current Signals Based Gear Fault Diagnosis
4. Fault Prognosis and Remaining Useful Life Prediction of Wind Turbine Gearboxes Using Current Signal Analysis
5. Sounds and acoustic emission-based early fault diagnosis of induction motor: A review study
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1. Refined time-shift multiscale slope entropy: a new nonlinear dynamic analysis tool for rotating machinery fault feature extraction;Nonlinear Dynamics;2024-08-05
2. Comprehensive Diagnosis of Localized Rolling Bearing Faults during Rotating Machine Start-Up via Vibration Envelope Analysis;Electronics;2024-01-16
3. Attention-aware temporal–spatial graph neural network with multi-sensor information fusion for fault diagnosis;Knowledge-Based Systems;2023-10
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