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A collection of unsupervised domain adaption algorithms for RUL estimation.

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RUL Adapt

Master Release Code style: black

This library contains a collection of unsupervised domain adaption algorithms for RUL estimation. They are provided as LightningModules to be used in PyTorch Lightning.

Currently, five approaches are implemented, including their original hyperparameters:

  • LSTM-DANN by Da Costa et al. (2020)
  • ADARUL by Ragab et al. (2020)
  • LatentAlign by Zhang et al. (2021)
  • TBiGRU by Cao et al. (2021)
  • Consistency-DANN by Siahpour et al. (2022)

Three approaches are implemented without their original hyperparameters:

  • ConditionalDANN by Cheng et al. (2021)
  • ConditionalMMD by Cheng et al. (2021)
  • PseudoLabels as used by Wang et al. (2022)

This includes the following general approaches adapted for RUL estimation:

  • Domain Adaption Neural Networks (DANN) by Ganin et al. (2016)
  • Multi-Kernel Maximum Mean Discrepancy (MMD) by Long et al. (2015)

Each approach has an example notebook which can be found in the examples folder.

Installation

This library is pip-installable. Simply type:

pip install rul-adapt

Contribution

Contributions are always welcome. Whether you want to fix a bug, add a feature or a new approach, just open an issue and a PR.