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.
This library is pip-installable. Simply type:
pip install rul-adaptContributions are always welcome. Whether you want to fix a bug, add a feature or a new approach, just open an issue and a PR.