ChangePoint-LSTM

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ChangePoint-LSTM

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Official code implementation for 'A change point detection integrated remaining useful life estimation model under variable operating conditions (2024)'

ChangePoint-LSTM 최신버젼 다운로드

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# A change point detection integrated remaining useful life estimation model under variable operating conditions [[Paper]](https://www.sciencedirect.com/science/article/pii/S0967066123004094) [[Cite]](#citation)
by Anushiya Arunan, Yan Qin, Xiaoli Li, Yuen Chau

## Abstract
![Image Description](https://raw.githubusercontent.com/en-research/ChangePoint-LSTM/main/figures/processflow_fig2.png)

By informing the onset of the degradation process, health status evaluation serves as a significant preliminary step for reliable remaining useful life (RUL) estimation of complex equipment. However, existing works rely on a priori knowledge to roughly identify the starting time of degradation, termed the change point, which overlooks individual degradation characteristics of devices working in variable operating conditions. Consequently, reliable RUL estimation for devices under variable operating conditions is challenging as different devices exhibit heterogeneous and frequently changing degradation dynamics. This paper proposes a novel temporal dynamics learning-based model for detecting change points of individual devices, even under variable operating conditions, and utilises the learnt change points to improve the RUL estimation accuracy. Using C-MAPSS turbofan engines as the case study, the proposed method improved the accuracy by 5.6% and 7.5% for two scenarios with six operating conditions, when compared to existing LSTM-based RUL estimation models that do not consider heterogeneous change points.

## Requirements
- Python 3.x
- Tensorflow 2.x
- Numpy
- Sklearn
- Pandas
- Shapely 
- Scipy
- Matplotlib
- h5py

## Dataset
A minimum working example dataset (FD001) is provided in ```CMAPSSData``` folder. For the complete dataset, please access it via [here](https://data.nasa.gov/Aerospace/CMAPSS-Jet-Engine-Simulated-Data/ff5v-kuh6/data).

## Code
The change point detection module is framework-agnostic (i.e., can be added to either a Pytorch or Tensorflow based RUL estimation model). The RUL estimation model is currently a Tensorflow 2 implementation.

To train and evaluate the change point detection and RUL estimation model with default hyperparameters, run ```train.py```.

:bulb: Useful tip: To plot and visualize the monitoring statistics of individual engines, uncomment line ```351``` in ```CVAfunctions.py``` to activate ```statistic_plot``` function before running ```train.py```. However, note that this will save a file of the plot for each engine in the local directory (adjust saving option accordingly).

## Citation
If you found this work useful, please consider citing:
```
@article{arunan2024change,
  title={A change point detection integrated remaining useful life estimation model under variable operating conditions},
  author={Arunan, Anushiya and Qin, Yan and Li, Xiaoli and Yuen, Chau},
  journal={Control Engineering Practice},
  volume={144},
  pages={105840},
  year={2024},
  publisher={Elsevier},
  doi = {https://doi.org/10.1016/j.conengprac.2023.105840}
}
```

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