This secretive base hidden in the outskirts of Appalachia, with its curvy terrain covered in thick deciduous forests and snaked with fuel pipes leading to massive concrete bunkers, is the type of place the Empire would hide a Death Star’s force field generator.
But this is Ohio, not a moon of Endor, so the portly, furry inhabitants here are beavers, not Ewoks, and the bleeding edge engines assembled and tested here will end up powering Boeing’s newest commercial airliners, not Imperial Star Destroyers.
General Electric has used this 6,700-acre expanse called the Peebles Test Operation—located an hour east of GE Aviation’s Cincinnati headquarters—since the 1950s, first as a rocket testing site, then for military and commercial aircraft engines.
Its isolated position has buffered it from prying eyes and, more importantly for neighbors, from the thunderous scream of the 1,800 jets engines tested here every year to meet FAA and European Aviation Safety Agency requirements.
|At its unassuming 6,700-acre Peebles Test Operation in the foothills of Southern
Ohio, GE is developing the world's most sophisticated commercial jet engines.
Photo: GE Aviation
Prior to sending an engine to the airframer, Peebles runs a “break-in” test, running the engine through take-off, climb, cruise, and landing. For the larger engines, Peebles has two massive indoor testing spaces connected to control rooms with big screens, flashing buttons, and shiny levers. The near empty rooms themselves are about 40 ft. high, with one wall just a giant computer screen and another has a giant black hole leading to an exhaust stack, about the size of the 111-in. wide engine hanging in front of it. The engine sucks the air through the screen and funnels it into the fan, with the exhaust going into the stack. The auto coupling plate, the wired harness the engine hangs from, funnels all the sensor data to the control room for analysis. This is what it’s really all about there.
|During testing, continuous data from this production GEnx jet engine is fed to the
control room to detect any anomalies. It also looks like the inside of the Death Star.
Photo: John Hitch
“Getting the data and getting the data analysis has been the biggest key,” explains Deborah Case, GE Aviation media relations representative and occasional Peebles tour guide.
This is true in terms of ensuring each jet engine is ready for takeoff, and in GE’s strategy to provide industrial companies the world over with a new method to harness the important big data that could otherwise be lost in the wind. The crux to this plan is Predix, the Industrial Internet platform that represents GE’s new hope to digitally terraform the world into brilliant, efficient place.
“It’s almost like remote diagnostics on steroids,” explains Case of the Predix Platform as a Service (PaaS), which has been used by GE internally for four years. GE Aviation and Peebles had a big part in its genesis, but Predix has now flown the nest to start managing the rest of GE’s $1 trillion in assets.
The big news now is that it’s available to third-party developers.
This isn’t the IoT platform to link your phone to your thermostat; this multi-service, cloud-based platform is meant to analyze in a manageable way the giant petabyte sized hunks of time series data streaming in from power plants or wind turbines, and facilitate the user in creating efficient solutions based off that information.
Cisco estimates that only 3% of industrial data is used in a meaningful way. Predix’s lofty goal is to allow a company to capture all that new data and use it in a meaningful way.
“People like Google and Facebook, they deal with huge amounts of data, but that data is usually pictures of cats and videos and large amounts of text searches, but mainly pictures of cats it seems these days,” says Jon Dunsdon, GE Aviation CTO. “Predix provides what engineers need and delivers it in a way that makes sense to them.”
This is what upgrades it from Internet of Things to Internet of Important Things. Dunsdon says these third-party software developers are given a “contextually relevant” API to clean up data and detect anomalies, with a strong foundation already laid by the conglomerate, “so you don’t need to code those up.”