jiaxiang-cheng/PyTorch-LSTM-for-RUL-Prediction

PyTorch implementation of remaining useful life prediction with long-short term memories (LSTM), performing on NASA C-MAPSS data sets. Partially inspired by Zheng, S., Ristovski, K., Farahat, A., & Gupta, C. (2017, June). Long short-term memory network for remaining useful life estimation.

Remaining Useful Life Prediction with LSTM

PyTorch implementation of remaining useful life (RUL) prediction with LSTM, with evaluations on NASA C-MAPSS engine data sets. Partially inspired by Zheng, S., Ristovski, K., Farahat, A., & Gupta, C. (2017, June). Long short-term memory network for remaining useful life estimation.
Author: Jiaxiang Cheng, Nanyang Technological University, Singapore

Environment

python==3.8.10
numpy~=1.20.2
pandas~=1.2.5
matplotlib~=3.3.4
pytorch==1.9.0

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: 21, loss: 3076.69349, rmse: 27.08139
Epoch: 22, loss: 2955.86564, rmse: 24.61716
Epoch: 23, loss: 2841.80114, rmse: 23.69018
Epoch: 24, loss: 2779.35199, rmse: 23.40924

...

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 stored in the folder _trials.

Citation & License