Predict the remaining useful life of an aircraft engine

This experiment aims to build a regression model to predict the RUL (Remaining Useful Life) of a specific aircraft engine, including data preprocessing, feature engineering, model building and evaluation steps. The dataset in this experiment was used for the prognostics challenge competition at the International Conference on Prognostics and Health Management (PHM08) and can be obtained from NASA Ames Prognostics Data Repository [1]. This data was generated using C-MAPSS, the commercial version of MAPSS (Modular Aero-Propulsion System Simulation) software. This software provides a flexible turbofan engine simulation environment to conveniently simulate the health, control and engine parameters. This dataset is the train.txt in the original competition datasets. It consists of multiple multivariate time series with "cycle" as the unit, together with 21 sensor readings for each cycle. Each time series can be considered from a different engine of the same type. Each engine is assumed to start with different degrees of initial wear and manufacturing variation, and this information is unknown to the user. In this simulated data, the engine is assumed operating normally at the start of each time series. It starts to degrade at some point during the series of the operating cycles. The degradation progresses and grows in magnitude. When a predefined threshold is reached it is regarded that the engine is not safe to be operated any more. In other words, the last cycle in each time series can be considered as the failure point of the corresponding engine. [1] A. Saxena and K. Goebel (2008). "PHM08 Challenge Data Set", NASA Ames Prognostics Data Repository (http://ti.arc.nasa.gov/tech/dash/pcoe/prognostic-data-repository), NASA Ames Research Center, Moffett Field, CA Created by a Microsoft Employee