Predictive Maintenance

David Smith shows off a predictive maintenance gallery for dealing with aircraft engines:

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-Royceto 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.

Check out the data set and play around a bit.

Related Posts

ggplot2 Geoms And Aesthetics

Tyler Rinker digs into ggplot2’s geoms and aesthetics: I thought it my be fun to use the geoms aesthetics to see if we could cluster aesthetically similar geoms closer together. The heatmap below uses cosine similarity and heirarchical clustering to reorder the matrix that will allow for like geoms to be found closer to one […]

Read More

Legible Function Chaining In R

John Mount shows a few techniques for legible function chaining with R: The dot intermediate convention is very succinct, and we can use it with base R transforms to get a correct (and performant) result. Like all conventions: it is just a matter of teaching, learning, and repetition to make this seem natural, familiar and legible. My […]

Read More


May 2016
« Apr Jun »