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HRCAE: A new unsupervised anomaly detection method for machine tools under noises

Our operating environment

  • Python 3.8
  • pytorch 1.10.1
  • and other necessary libs

Guide

  • This repository provides a concise framework for unsupervised anomaly detection for machine tools under noises. It includes a demo dataset; the pre-processing process for the data and the model proposed in the paper. We have also integrated 2 baseline methods for comparison.
  • You just need to run start_procedure.py. You can also adjust the structure and parameters of the model to suit your needs.

Pakages

  • data contians a demo dataset
  • datasets contians the pre-processing process and the type of added noise for the data
  • models contians the proposed model and 2 base models
  • utils contians train&val&test processes

Citation

If you use our work as a comparison model, please cite:

@paper{HRCAE,
  title = {Hybrid robust convolutional autoencoder for unsupervised anomaly detection of machine tools under noises},
  author = {Shen Yan, Haidong Shao, Yiming Xiao, Bin Liu, Jiafu Wan},
  journal = {Robotics and Computer-Integrated Manufacturing},
  volume = {79},
  pages = {102441},
  year = {2023},
  doi = {https://doi.org/10.1016/j.rcim.2022.102441},
  url = {https://www.sciencedirect.com/science/article/pii/S0736584522001259},
}

If our work is useful to you, please star it, it is the greatest encouragement to our open source work, thank you very much!

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