A Unified Label Noise-Tolerant Framework of Deep Learning-Based Fault Diagnosis via a Bounded Neural Network-Reference-Cited by-同舟云学术

A Unified Label Noise-Tolerant Framework of Deep Learning-Based Fault Diagnosis via a Bounded Neural Network

Published:2024 Issue: Volume:73 Page:1-15
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

He Sudao¹^ORCID,Ao Wai Kei¹^ORCID,Ni Yi-Qing¹^ORCID

Affiliation:

1. Hong Kong Branch of National Rail Transit Electrification and Automation Engineering Technology Research Center and the Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong

Funder

The Hong Kong Polytechnic University Start-up Fund for RAPs under the Strategic Hiring Scheme

ITC to the Hong Kong Branch of Chinese National Rail Transit Electrification and Automation Engineering Technology Research Center

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/19/10367905/10462126.pdf?arnumber=10462126

Reference58 articles.

1. Explainable Intelligent Fault Diagnosis for Nonlinear Dynamic Systems: From Unsupervised to Supervised Learning

2. Multi-Information Fusion Fault Diagnosis of Bogie Bearing Under Small Samples via Unsupervised Representation Alignment Deep Q-Learning

3. A Systematic Review on Imbalanced Learning Methods in Intelligent Fault Diagnosis

4. Modern Tendencies in Vehicle-Based Condition Monitoring of the Railway Track

5. A Review of Fault Detection and Diagnosis for the Traction System in High-Speed Trains

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