Fault Diagnosis of the Rolling Bearing by a Multi-Task Deep Learning Method Based on a Classifier Generative Adversarial Network.

Shen Z, Kong X, Cheng L, Wang R, Zhu Y

Open source

DOI
10.3390/s24041290
Published
2024 Feb 17
Container
Sensors (Basel, Switzerland)
Publisher
Not recorded
Open access
yes

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BibTeX

@article{allodium:10.3390/s24041290,
  title = {Fault Diagnosis of the Rolling Bearing by a Multi-Task Deep Learning Method Based on a Classifier Generative Adversarial Network.},
  author = {Shen Z and Kong X and Cheng L and Wang R and Zhu Y},
  year = {2024},
  journal = {Sensors (Basel, Switzerland)},
  doi = {10.3390/s24041290},
  url = {https://doi.org/10.3390/s24041290}
}

RIS

TY  - JOUR
TI  - Fault Diagnosis of the Rolling Bearing by a Multi-Task Deep Learning Method Based on a Classifier Generative Adversarial Network.
AU  - Shen Z
AU  - Kong X
AU  - Cheng L
AU  - Wang R
AU  - Zhu Y
PY  - 2024
JO  - Sensors (Basel, Switzerland)
DO  - 10.3390/s24041290
UR  - https://doi.org/10.3390/s24041290
ER  -

APA

Z, S., X, K., L, C., R, W., & Y, Z. (2024). Fault Diagnosis of the Rolling Bearing by a Multi-Task Deep Learning Method Based on a Classifier Generative Adversarial Network.. Sensors (Basel, Switzerland). https://doi.org/10.3390/s24041290

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