Research on fault diagnosis method of wheelset bearing based on IESK deconvolution and AC-LPC resonance peak estimation-Reference-Cited by-同舟云学术

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Research on fault diagnosis method of wheelset bearing based on IESK deconvolution and AC-LPC resonance peak estimation

Published:2023-06-08 Issue:7 Volume:45 Page:
ISSN:1678-5878
Container-title:Journal of the Brazilian Society of Mechanical Sciences and Engineering
language:en
Short-container-title:J Braz. Soc. Mech. Sci. Eng.

Author:

Zhang Xiong^ORCID,Dong Fan,Wu Wenbo,Li Jialu,Wan Shuting,Gu Xiaohui

Funder

National Natural Science Foundation of China

Natural Science Foundation of Hebei Province

Publisher

Springer Science and Business Media LLC

Subject

Mechanical Engineering,General Engineering,Aerospace Engineering,Automotive Engineering,Industrial and Manufacturing Engineering,Applied Mathematics

Link

https://link.springer.com/content/pdf/10.1007/s40430-023-04276-w.pdf

Reference28 articles.

1. Zhang YX, Zhu DC, Zhao L (2021) Fault diagnosis of rolling element bearing using ACYCBD based cross correlation spectrum. J Braz Soc Mech Sci Eng 46(10):1–18. https://doi.org/10.1007/s40430-021-02955-0

2. Zhou PC et al (2022) Impulses recovery technique based on high oscillation region detection and shifted rank-1 reconstruction-its application to bearing fault detection. IEEE Sens J 22(8):8084–8093. https://doi.org/10.1109/JSEN.2022.3159116

3. Liu ZL et al (2019) Modeling of the safe region based on support vector data description for health assessment of wheelset bearings. Appl Math Model 73:19–39. https://doi.org/10.1016/j.apm.2019.03.040

4. Peng DD et al (2019) A novel deeper one-dimensional CNN with residual learning for fault diagnosis of wheelset bearings in high-speed trains. IEEE Access 7:10278–10293. https://doi.org/10.1109/ACCESS.2018.2888842

5. Giannouli E et al (2021) Detection and evaluation of rolling stock wheelset defects using acoustic emission. Insight 63(7):403–408. https://doi.org/10.1784/insi.2021.63.7.403

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