Predictive Modeling of Turbofan Degradation (Part 1)

One of the recent projects I have finished was predictive modeling of turbofan engine degradation.  The goal is to predict the remaining useful life, or the remaining number of cycles, until a turbofan engine can no longer perform up to requirements from information from sensors on the turbofan and the number of cycles completed.  The original dataset was published by NASA’s Prognostics Center of Excellence [1].

640px-Turbofan_operation.svg[2]

I particularly liked this data set as it gave me a chance to combine some of my favorite things: data mining, machine learning, data from sensors, and physical things.  In general the problem falls under the engineering domain of prognostics.  The data simulates different degrees of initial wear and manufacturing variability, for a series of engines.  Each engine develops a fault at some point, speeding up the degradation.  From the data one knows the operational settings of the turbofan for that cycle and a snapshot measurement of each of 21 sensors during that cycle. Sensor noise and bias is also simulated.

There are four datasets available:  FD001 with 1 operating conditions and 1 failure mode, FD002 with 6 operating conditions and 1 failure mode, FD003 with 1 operating condition and 2 failure modes, and FD004 with 6 operating conditions and 2 failure modes.

Now that the ground work has been set, the next part will focus on exploratory data analysis for FD001.  Over the course of this series I will investigate and model the first (simplest) and last (most complex) data sets, including the use of Kalman filters for ensembling multiple machine learning models together and tips and tricks for modeling and cross validation of time series data.

To be continued…


 

All of the code for this work is available my GitHub  repository for this project.  I have previously presented this material and MLConf Atlanta and Unstructured Data Science Pop-Up Seattle, with the support of H2O.ai.

[1] A. Saxena and K. Goebel (2008). “Turbofan Engine Degradation Simulation Data Set”, NASA Ames Prognostics Data Repository (http://ti.arc.nasa.gov/project/prognostic-data-repository), NASA Ames Research Center, Moffett Field, CA

[2] Image courtesy “Turbofan operation” by K. Aainsqatsi – Own work. Licensed under CC BY 2.5 via Commons – https://commons.wikimedia.org/wiki/File:Turbofan_operation.svg#/media/File:Turbofan_operation.svg

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