HungVu307 / Few-shot-via-ensembling-Transformer-with-Mahalanobis-distance

This is official code for paper "Few-Shot Bearing Fault Diagnosis via Ensembling Transformer-based Model with Mahalanobis Distance Metric Learning from Multiscale Features". IEEE Transactions on Instrumentation and Measurement (Accepted)
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Few-shot-via-ensembling-Transformer-with-Mahalanobis-distance

Link to paper

We provide a Pytorch implement code of paper "Few-Shot Bearing Fault Diagnosis via Ensembling Transformer-based Model with Mahalanobis Distance Metric Learning from Multiscale Features" accepted by IEEE Transactions on Instrumentation and Measurement.

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Prerequisites

Note, if you use these datasets, please cite the corresponding papers. (Feel free to contact me if you need PU dataset in .pt file)

Getting Started

Contact

Please feel free to contact me via email hung.vm195780@sis.hust.edu.vn or vumanhhung07.work@gmail.com if you need anything related to this repo!

Citation

If you feel this code is useful, please give us 1 ⭐ and cite our paper.

@article{vu2024few,
  title={Few-Shot Bearing Fault Diagnosis via Ensembling Transformer-based Model with Mahalanobis Distance Metric Learning from Multiscale Features},
  author={Vu, Manh-Hung and Nguyen, Van-Quang and Tran, Thi-Thao and Pham, Van-Truong and Lo, Men-Tzung},
  journal={IEEE Transactions on Instrumentation and Measurement},
  year={2024},
  publisher={IEEE}
}
@misc{vu2024few,
  author = {Vu, Manh-Hung and Nguyen, Van-Quang and Tran, Thi-Thao and Pham, Van-Truong and Lo, Men-Tzung},
  title = {Few-Shot Bearing Fault Diagnosis via Ensembling Transformer-based Model with Mahalanobis Distance Metric Learning from Multiscale Features},
  year = {2024},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/HungVu307/Few-shot-via-ensembling-Transformer-with-Mahalanobis-distance}},
}