Fault Diagnosis With Robustness and Lightweight Synergy Under Noisy Environment
Author:
Affiliation:
1. School of Electronic and Information Engineering, Taiyuan University of Science and Technology, Taiyuan, China
2. Shanxi Key Laboratory of Advanced Control and Equipment Intelligence, Taiyuan, China
Funder
Shanxi Provincial Key Research and Development Project
Fundamental Research Program of Shanxi Province
Graduate Education Innovation Project in Taiyuan University of Science and Technology
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Instrumentation
Link
http://xplorestaging.ieee.org/ielx7/7361/10183770/10141574.pdf?arnumber=10141574
Reference39 articles.
1. Multi-level features fusion network-based feature learning for machinery fault diagnosis
2. Multi-class brain tumor classification using residual network and global average pooling
3. Fault Diagnosis of Rotating Machinery Based on 1D–2D Joint Convolution Neural Network
4. Resource efficient activation functions for neural network accelerators
5. Dually attentive multiscale networks for health state recognition of rotating machinery
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1. A novel multi-sensor hybrid fusion framework;Measurement Science and Technology;2024-05-02
2. Rotating Machinery Fault Diagnosis with Limited Multisensor Fusion Samples by Fused Attention-Guided Wasserstein GAN;Symmetry;2024-03-01
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