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Predicting Remaining Useful Life

Featuretools NASA

The general setup for the problem is a common one: we have a single table of sensor observations over time. Now that collecting information is easier than ever, most industries have already generated time-series type problems by the way that they store data. As such, it is crucial to be able to handle data in this form. Thankfully, built-in functionality from Featuretools handles time varying data well.

We'll demonstrate an end-to-end workflow using a Turbofan Engine Degradation Simulation Data Set from NASA. This notebook demonstrates a rapid way to predict the Remaining Useful Life (RUL) of an engine using an initial dataframe of time-series data. There are three sections of the notebook:

  1. Understand the Data
  2. Generate features
  3. Make predictions with Machine Learning

To run the notebooks, you need to download the data yourself. Download and unzip the file from Then create a 'data' directory and place the files in the 'data' directory.


  • Quickly make end-to-end workflow using time-series data
  • Find interesting automatically generated features
  • An advanced notebook using custom primitives and hyper-parameter tuning

Running the tutorial

  1. Clone the repo

    git clone
  2. Install the requirements

    pip install -r requirements.txt

    You will also need to install graphviz for this demo. Please install graphviz according to the instructions in the Featuretools Documentation

  3. Download the data

    The data is from the NASA Turbofan Engine Degradation Simulation Data Set and is available here

    To run the notebooks, place the following files in the 'data' directory: train_FD004.txt, test_FD004.txt, RUL_FD004.txt

  4. Run the Tutorials notebooks:

    jupyter notebook

    The script contains a number of useful helper functions.

Feature Labs


Featuretools is an open source project created by Feature Labs. To see the other open source projects we're working on visit Feature Labs Open Source. If building impactful data science pipelines is important to you or your business, please get in touch.


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Predict remaining useful life of a component based on historical sensor observations using automated feature engineering







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