chendingliang/PyTorch-CNN-for-RUL-Prediction

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