• Core codes for the paper "Hybrid robust convolutional autoencoder for unsupervised anomaly detection of machine tools under noises"
• Created by Shen Yan, Haidong Shao, Yiming Xiao, Bin Liu, Jiafu Wan.
• Journal: Robotics and Computer-Integrated Manufacturing

• Python 3.8
• pytorch 1.10.1
• and other necessary libs

• 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.

• 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

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!

• [email protected]