Update extracted & processed N-CMAPSS dataset, 6/10/2023

To promote relevant researches on domain adaptive RUL prediction in the community, we provide the N-CMAPSS dataset used in this paper, with 7 domains readily available. Download here.

Pytorch implementation for Domain Adaptive Remaining Useful Life Prediction with Transformer, https://doi.org/10.1109/TIM.2022.3200667

• Prognostic health management (PHM) has become a crucial part in building highly automated systems, whose primary task is to precisely predict the remaining useful life (RUL) of the system. In this paper, we leverage domain adaptation for RUL prediction and propose a novel method by aligning distributions at both the feature level and the semantic level. The proposed method facilitates a large improvement of model performance as well as faster convergence. Besides, we propose to use Transformer as backbone, which can capture long-term dependency more efficiently. We test our model on CMAPSS dataset and its newly published variant N-CMAPSS provided by NASA, achieving state-of-the-art results on both source-only RUL prediction and domain adaptive RUL prediction tasks.

Environment

This is a strict constraint!

• pytorch==1.10
• torchvision==0.11.0

  Dataset

• Unzip CMAPSS.zip into CMAPSS folder, which contains processed CMAPSS dataset used in this code.

  Usage

• train on CMAPSS
  python train_cmapss.py --source $S --target $T
  where $S is source domain, $T is target domain. Domains include "FD001,FD002,FD003,FD004". Trained models are saved to /online.
• evaluate on CMAPSS
  You can evaluate our best performing models saved in folder save/final by running:
  python validation_cmapss.py --source $S --target $T
  where $S is source domain, $T is target domain. Domains include "FD001,FD002,FD003,FD004".

  Results

  

  Citation

  @article{li2022domain,
  title={Domain Adaptive Remaining Useful Life Prediction With Transformer},
  author={Li, Xinyao and Li, Jingjing and Zuo, Lin and Zhu, Lei and Shen, Heng Tao},
  journal={IEEE Transactions on Instrumentation and Measurement},
  volume={71},
  pages={1--13},
  year={2022},
  publisher={IEEE}
  }