An implementation with Transformer encoder and convolution layers with PyTorch for remaining useful life prediction.
Author: Jiaxiang Cheng, Nanyang Technological University, Singapore
Simply run python train.py --dataset FD001. And you will get the training loss and testing result for each epoch where the RMSE is from the test set:
Epoch: 0, loss: 9474.43470, RMSE: 61.11946
Epoch: 1, loss: 5858.27227, RMSE: 46.03318
Epoch: 2, loss: 3208.53410, RMSE: 29.78244
Epoch: 3, loss: 1310.71390, RMSE: 22.94705
...
The testing is conducted for each epoch as the data set is not large so it's no big deal but you may remove them and only do the evaluation after finishing the training epochs.
python==3.8.8
numpy==1.20.1
pandas==1.2.4
matplotlib==3.3.4
pytorch==1.8.1
This work is inpired by Mo, Y., Wu, Q., Li, X., & Huang, B. (2021). Remaining useful life estimation via transformer encoder enhanced by a gated convolutional unit. Journal of Intelligent Manufacturing, 1-10.
