the project main theme is to develop a model to perform predictive maintenance on a jet engine. the predictive maintenance approach used here is data-driven, meaning that data collected from the operational jet engine is used to perform predictive maintenance modeling. to be specific, the project aim is to build a predictive model to estimate the Remaining Useful Life ( RUL) of a jet engine based on run-to-failure data of a fleet of similar jet engines

There should be no necessary libraries to run the code here beyond the Anaconda distribution of Python. The code should run with no issues using Python versions 3.*.

my motivation behind this small project is to enhance my capability in making models that can be used in predictive maintenance.

The jupyter notebook available here contains the modeling and data exploration Data folder contains the datasets

the main findings of the code can be found at the Medium post available here.

the data used in this project was obtained from Prognostics CoE at NASA Ames

the algorithm idea follows closely : T. Wang, J. Yu, D. Siegel, J. Lee, "A similarity-based prognostics approach for remaining useful life estimation of engineered systems", Proc. Int. Conf. Prognostics Health Manage., pp. 1-6, Oct. 2008. Similarity-Based Remaining Useful Life Estimation: https://www.mathworks.com/help/predmaint/examples/similarity-based-remaining-useful-life-estimation.html#SimilarityBasedRULExample-10