P2IFormer: A Multi-Granularity Patch-to-Image Embedding Model for Fault Diagnosis of High-Speed Train Axle-Box Bearings.

Ma W, Zhang C, Chen L, Wang Z, Fan X, Cui Y.

Open source

DOI: 10.3390/s25165138
Published: 2025-08-19
Container: Sensors (Basel)
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.3390/s25165138,
  title = {P2IFormer: A Multi-Granularity Patch-to-Image Embedding Model for Fault Diagnosis of High-Speed Train Axle-Box Bearings.},
  author = {Ma W and  Zhang C and  Chen L and  Wang Z and  Fan X and  Cui Y.},
  year = {2025},
  journal = {Sensors (Basel)},
  doi = {10.3390/s25165138},
  url = {https://doi.org/10.3390/s25165138}
}

RIS

TY  - JOUR
TI  - P2IFormer: A Multi-Granularity Patch-to-Image Embedding Model for Fault Diagnosis of High-Speed Train Axle-Box Bearings.
AU  - Ma W
AU  -  Zhang C
AU  -  Chen L
AU  -  Wang Z
AU  -  Fan X
AU  -  Cui Y.
PY  - 2025
JO  - Sensors (Basel)
DO  - 10.3390/s25165138
UR  - https://doi.org/10.3390/s25165138
ER  -

APA

W, M., C, Z., L, C., Z, W., X, F., & Y., C. (2025). P2IFormer: A Multi-Granularity Patch-to-Image Embedding Model for Fault Diagnosis of High-Speed Train Axle-Box Bearings.. Sensors (Basel). https://doi.org/10.3390/s25165138

Source records

europe-pmc · retrieved 2026-11-10T10:50:45.416Z
doaj · retrieved 2026-11-10T10:50:45.405Z