Predictive Maintenance for Aircraft Engines

May 25, 2016
By

(This article was first published on Revolutions, and kindly contributed to R-bloggers)

Recently, I wrote about how it's possible to use predictive models to predict when an airline engine will require maintenance, and use that prediction to avoid unpleasant (and expensive!) delays for passengers on the ground. Planes generate a lot of data that can be used to make such predictions: today’s engines have hundreds of sensors and signals that transmit gigabytes of data for each flight. If you have access to data like this, you can generate predictions using Microsoft Azure services using the Predictive Maintenance for Aerospace solution in the Cortana Intelligence Gallery. (If you don't have data but still want to play around with the solution, it will generate simulated data based on this public data set donated by NASA.) The solution automates the process of launching and configuring several Azure services as shown in the architecture diagram below.

Ca-topologies-maintenance-prediction

If you prefer the manual route, there's also a step-by-step walkthough on GitHub on deploying the Predictive Maintenance solution. Of particular interest to R users is the Predictive Maintenance Template for SQL Server R Services, which includes R code that runs in the SQL Server database to:

  • Predict the Remaining Useful Life or Time To Failure of an asset, such an an engine component
  • Predict if an asset will fail within certain time frame or within a specific time window

In each case, a number of different models are trained in R (decision forests, boosted decision trees, multinomial models, neural networks and poisson regression) and compared for performance; the best model is automatically selected for predictions. 

On a related note, Microsoft recently teamed up with aircraft engine manufacturer Rolls-Royce to help airlines get the most out of their engines. Rolls-Royce is turning to Microsoft's Azure cloud-based services — Stream Analytics, Machine Learning and Power BI — to make recommendations to airline executives on the most efficient way to use their engines in flight and on the ground. This short video gives an overview.

 

Cortana Intelligence Gallery: Predictive Maintenance for Aerospace solution

To leave a comment for the author, please follow the link and comment on their blog: Revolutions.

R-bloggers.com offers daily e-mail updates about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...



If you got this far, why not subscribe for updates from the site? Choose your flavor: e-mail, twitter, RSS, or facebook...

Comments are closed.

Search R-bloggers


Sponsors

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)