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 ...

To reduce computational complexity and to improve prediction accuracy, a deep learning method based on Sparse Denoising LSTM (SD-LSTM) is proposed in this paper. The sparse gate structure designed in ...

Using the sparse idea of Highway network to design a sparse denoising LSTM network that suppresses redundant neurons to achieve more accurate residual life (RUL) prediction. Different from the idea ...

NASA-CMAPSS-RUL-Prediction This project predicts the Remaining Useful Life (RUL) of aircraft engines using a Bidirectional LSTM model on the ASA C-MAPSS FD001 dataset.

The team proposed a prediction method based on the MLP-Mixer and Mixture of Expert (MMMe) model, for RUL prediction. Experimental results on NASA and CALCE public datasets show that the proposed ...