Fault Diagnosis With Bidirectional Guided Convolutional Neural Networks Under Noisy Labels-Reference-Cited by-同舟云学术

Fault Diagnosis With Bidirectional Guided Convolutional Neural Networks Under Noisy Labels

Published:2023-08-15 Issue:16 Volume:23 Page:18810-18820
ISSN:1530-437X
Container-title:IEEE Sensors Journal
language:
Short-container-title:IEEE Sensors J.

Author:

Zhang Kai¹^ORCID,Li Zhixuan¹^ORCID,Zheng Qing¹^ORCID,Ding Guofu¹^ORCID,Tang Baoping²^ORCID,Zhao Minghang³^ORCID

Affiliation:

1. School of Mechanical Engineering and the Technology and Equipment of Rail Transit Operation and Maintenance Key Laboratory of Sichuan Province, Southwest Jiaotong University, Chengdu, China

2. College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing, China

3. School of Ocean Engineering, Harbin Institute of Technology at Weihai, Weihai, China

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

Fundamental Research Funds for the Central Universities

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/7361/10217055/10171174.pdf?arnumber=10171174

Reference40 articles.

1. Manifold-Preserving Sparse Graph and Deviation Information Based Fisher Discriminant Analysis for Industrial Fault Classification Considering Label-Noise and Unobserved Faults

2. Novel Joint Transfer Network for Unsupervised Bearing Fault Diagnosis From Simulation Domain to Experimental Domain

3. Auto Labeling Methods Developed Through Semi-Weakly Supervised Learning in Prognostics and Health Management Applications for Rolling Ball Bearing

4. Applications of machine learning to machine fault diagnosis: A review and roadmap

5. Learning From Noisy Labels With Deep Neural Networks: A Survey

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1. An Adaptive Symmetric Loss in Dynamic Wide-Kernel ResNet for Rotating Machinery Fault Diagnosis Under Noisy Labels;IEEE Transactions on Instrumentation and Measurement;2024

2. A Unified Label Noise-Tolerant Framework of Deep Learning-Based Fault Diagnosis via a Bounded Neural Network;IEEE Transactions on Instrumentation and Measurement;2024

3. Sharpness-Aware Gradient Alignment for Domain Generalization With Noisy Labels in Intelligent Fault Diagnosis;IEEE Transactions on Instrumentation and Measurement;2024

4. Adaptive Dynamic Threshold Graph Neural Network: A Novel Deep Learning Framework for Cross-Condition Bearing Fault Diagnosis;Machines;2023-12-28

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