PyTorch CNN For RUL Prediction Save
PyTorch implementation of CNN for remaining useful life prediction. Inspired by Babu, G. S., Zhao, P., & Li, X. L. (2016, April). Deep convolutional neural network-based regression approach for estimation of remaining useful life. In International conference on database systems for advanced applications (pp. 214-228). Springer, Cham.
Deep CNN for Estimation of Remaining Useful Life
Inspired by Babu, G. S., Zhao, P., & Li, X. L. (2016, April). Deep convolutional neural network
based regression approach for estimation of remaining useful life. In International conference on database systems for
advanced applications (pp. 214-228). Springer, Cham.
Author: Jiaxiang Cheng, Nanyang Technological University, Singapore
Environment
python==3.8.8
pytorch==1.8.1
pandas==1.2.4
scikit-learn==0.23.2
numpy==1.20.1
matplotlib==3.3.4
scipy==1.6.2
Usage
You may simply give the following command for both training and evaluation:
python main.py
Then you will get the following running information:
...
Epoch : 30 loss : 3.285 RMSE = 34.636 Score = 14473
Epoch : 31 loss : 3.277 RMSE = 34.599 Score = 14815
Epoch : 32 loss : 3.269 RMSE = 34.95 Score = 12690
Epoch : 33 loss : 3.259 RMSE = 32.885 Score = 6656
Epoch : 34 loss : 3.25 RMSE = 32.354 Score = 5344
Epoch : 35 loss : 3.241 RMSE = 32.318 Score = 4898
...
As the model and data sets are not heavy, the evaluation will be conducted after each
training epoch to catch up with the performance closely.
The prediction results will be saved in the folder _trials.
Citation
License
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