A collection of unsupervised domain adaption approaches for RUL estimation.
Project description
RUL Adapt
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.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for rul_adapt-0.3.1-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | ec9a77bedc24ae1acd395fb206f7b4d2637994bc7049a81cab1d156fe7f72ede |
|
MD5 | d92382e1e43aaea05545b46197c46afd |
|
BLAKE2b-256 | c6b7eb0e4df8c7d19502ce2f2ba47169b715bc92be93d18285e6dede7f569d8c |