# Enhancing Remaining Useful Life Prediction with Ensemble Multi-Term Fourier Graph Neural Networks


### Running the Codes for C-MAPSS

- FD001-- python main_RUL_Single_OC.py dataset '001' -- model 'FGN' -- seq length [30, 60, 90, 120, 150, 180] -- embed size 128 -- batch size 256 --train True

- FD003-- python main_RUL_Single_OC.py dataset '003' -- model 'FGN' -- seq length [30, 60, 90, 120, 150, 180, 210, 240] -- embed size 128 -- batch size 256 --train True


- FD002-- python main_RUL_Multi_OC.py dataset '002' -- model 'FGN' -- seq length [20, 40, 60, 80, 100, 120, 140, 160, 180] -- embed size 128 -- batch size 256 --train True

- FD004-- python main_RUL_Multi_OC.py dataset '004' -- model 'FGN' -- seq length [18, 40, 62, 84, 106, 128, 150, 172, 194] -- embed size 128 -- batch size 256 --train True


### Running the Codes for N-CMAPSS

- DS02-- python dataloader_ncmapss.py dataset 'DS02' -- model 'FGN' -- sampling 100 -- feature_size 20 -- sequence_length 100 -- embed size 32 -- batch size 64 -- train_epochs 50 --train True

- DS02-- python dataloader_ncmapss.py dataset 'DS02' -- model 'FGN' -- sampling 100 -- feature_size 20 -- sequence_length 200 -- embed size 32 -- batch size 64 -- train_epochs 50 --train True

