Rail Crack Detection Using Optimal Local Mean Decomposition and Cepstral Information Coefficient Based on Electromagnetic Acoustic Emission Technology-Reference-Cited by-同舟云学术

Rail Crack Detection Using Optimal Local Mean Decomposition and Cepstral Information Coefficient Based on Electromagnetic Acoustic Emission Technology

Published:2024 Issue: Volume:73 Page:1-12
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Chang Yongqi¹^ORCID,Zhang Xin¹^ORCID,Shen Yi¹^ORCID,Song Shuzhi¹^ORCID,Song Qinghua¹^ORCID,Cui Jiazhong¹^ORCID,Jie Huamin²^ORCID,Zhao Zhenyu³^ORCID

Affiliation:

1. Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, China

2. School of Electrical and Electronic Engineering, Nanyang Technological University, Jurong West, Singapore

3. Department of Electrical and Computer Engineering, National University of Singapore, Queenstown, Singapore

Funder

Natural Science Foundation of Heilongjiang Province

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/19/10367905/10464366.pdf?arnumber=10464366

Reference38 articles.

1. MtlrNet: An Effective Deep Multitask Learning Architecture for Rail Crack Detection

2. Online Dynamic Detection of Hazardous Cracks in Train Wheels by Probe Combination: Based on Classical Piezoelectric Ultrasonic Technology

3. Transverse Crack Detection of Rail Base Considering Wedge-Like Structure and Using a Bulk-Wave Electromagnetic Acoustic Transducer

4. Statistical Causal Analysis of Freight-Train Derailments in the United States

5. An Improved Structural Health Monitoring Method Utilizing Sparse Representation for Acoustic Emission Signals in Rails

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