This implementation follows the research paper published by Zheng et. al. [1] on predicting the Remaining Useful Life of a complex physical system. The dataset used for this study is the NASA Turbofan Jet Engine Data Set [4,5].
[1] Zheng et. al., Long Short-Term Memory Network for Remaining Useful Life Estimation,
IEEE International Conference on Prognostics and Health Management (ICPHM), 2017
[2] https://machinelearningmastery.com/how-to-develop-convolutional-neural-network-models-for-time-series-forecasting/
[3] https://www.kaggle.com/vinayak123tyagi/damage-propagation-modeling-for-aircraft-engine
[4] A. Saxena, K. Goebel, D. Simon, and N. Eklund, Damage Propagation Modeling for Aircraft Engine Run-to-Failure Simulation,
Proceedings of the 1st International Conference on Prognostics and Health Management (PHM08), Denver CO, Oct 2008.
[5] https://www.kaggle.com/behrad3d/nasa-cmaps