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January 11 2013

Defining the industrial Internet

We’ve been collecting threads on what the industrial Internet means since last fall. More case studies, company profiles and interviews will follow, but here’s how I’m thinking about the framework of the industrial Internet concept. This will undoubtedly continue to evolve as I hear from more people who work in the area and from our brilliant readers.

The crucial feature of the industrial Internet is that it installs intelligence above the level of individual machines — enabling remote control, optimization at the level of the entire system, and sophisticated machine-learning algorithms that can work extremely accurately because they take into account vast quantities of data generated by large systems of machines as well as the external context of every individual machine. Additionally, it can link systems together end-to-end — for instance, integrating railroad routing systems with retailer inventory systems in order to anticipate deliveries accurately.

In other words, it’ll look a lot like the Internet — bringing industry into a new era of what my colleague Roger Magoulas calls “promiscuous connectivity.”

Optimization becomes more efficient as the size of the system being optimized grows (in theory). Your software can take into account lots of machines, learning from a much larger training set and then optimizing both within the machine and for the group of machines working together. Think of a wind farm. There are certain optimizations you need to make at the machine level: the turbine turns itself to face into the wind, the blades adjust themselves through every cycle in order to account for flex and compression, and the machine shuts down during periods of dangerously high wind.

System-wide optimization means that when you can operate each turbine in a way that minimizes air disruption to other turbines (these things create wake, just like an airplane, that can disrupt the operation of nearby turbines). When you need to increase or decrease power output across the whole farm, you can do it across lots of machines in a way that minimizes wear (i.e., curtail each machine by 5% or cut off 5% of your machines, or something in between depending on differential output and the impact of different speeds on machine wear). And by gathering data from thousands of machines, you can develop highly-detailed optimization plans.

By tying machines together, the industrial Internet will encourage “platformization.” Cars have several control systems, and until very recently they’ve been linked by point-to-point connections: when you move the windshield-wiper lever, it actuates a switch that’s connected to a small PLC that operates the windshield wipers. The brake pedal is part of the chassis-control system, and it’s connected by cable or hydraulics to the brake pads, with an electronic assist somewhere in the middle. The navigation system and radio are part of the same telematics platform, but that platform is not linked to, say, the steering wheel.

The car as enabled by the industrial Internet will be a platform — a bus, in the computing sense — built by the car manufacturer, with other systems communicating with each other through the platform. The brake pedal is an actuator that sends a “brake” signal to the car’s brake controller. The navigation system is able to operate the steering wheel and has access to the same brake controller. Some of these systems will be driven by third-party-built apps that sit on top of the platform.

This will take some time to happen in cars because it takes 10 or 15 years to renew the American auto fleet, because cars are maintained by a vast network of independent mechanics that need change to happen slowly, and because car development works incrementally.

But it’s already happening in commercial aircraft, which often come from clean-sheet designs (as with the Boeing 787 and Airbus A350), and which are maintained under very different circumstances than passenger cars. In Bombardier’s forthcoming C-series midsize jet, for instance, the jet engines do nothing but propel the plane and generate electricity (they don’t generate hydraulic pressure or compress air for the cabin; these are handled by electrically-powered compressors). The plane acts as a giant hardware platform on which all sorts of other systems sit: the landing-gear switch communicates with the landing gear through the aircraft’s bus, rather than by direct connection to the landing gear’s PLC.

The security implications of this sort of integration — in contrast to effectively air-gapped isolation of systems — are obvious. The industrial Internet will need its own specially-developed security mechanisms, which I’ll look into in another post.

The industrial Internet makes it much easier to deploy and harvest data from sensors, which goes back to the system-wide intelligence point above. If you’re operating a wind farm, it’s useful to have wind-speed sensors distributed across the country in order to predict and anticipate wind speeds and directions. And because you’re operating machine-learning algorithms at the system-wide level, you’re able to work large-scale sensor datasets into your system-wide optimization.

That, in turn, will help the industrial Internet take in previously-uncaptured data that’s made newly useful. Venkatesh Prasad, from Ford, pointed out to me that the windshield wipers in your car are a sort of human-actuated rain API. When you turn on your wipers, you’re acting as a sensor — you see water on your windshield, in a quantity sufficient to cause you to want your wipers on, and you set your wipers to a level that’s appropriate to the amount of water on your windshield.

In isolation, all you’re doing is turning on your windshield wipers. But if your car is networked, then it can send a signal to a cloud-based rain-detection service that geocorrelates your car with nearby cars whose wipers are on and makes an assumption about the presence of rain in the area and its intensity. That service could then turn on wipers in other cars nearby or do more sophisticated things — anything from turning on their headlights to adjusting the assumptions that self-driving cars make about road adhesion.

This is an evolving conversation, and I want to hear from readers. What should be included in the definition of the industrial Internet? What examples define, for you, the boundaries of the field?


This is a post in our industrial Internet series, an ongoing exploration of big machines and big data. The series is produced as part of a collaboration between O’Reilly and GE.

Related

Defining the industrial Internet

We’ve been collecting threads on what the industrial Internet means since last fall. More case studies, company profiles and interviews will follow, but here’s how I’m thinking about the framework of the industrial Internet concept. This will undoubtedly continue to evolve as I hear from more people who work in the area and from our brilliant readers.

The crucial feature of the industrial Internet is that it installs intelligence above the level of individual machines — enabling remote control, optimization at the level of the entire system, and sophisticated machine-learning algorithms that can work extremely accurately because they take into account vast quantities of data generated by large systems of machines as well as the external context of every individual machine. Additionally, it can link systems together end-to-end — for instance, integrating railroad routing systems with retailer inventory systems in order to anticipate deliveries accurately.

In other words, it’ll look a lot like the Internet — bringing industry into a new era of what my colleague Roger Magoulas calls “promiscuous connectivity.”

Optimization becomes more efficient as the size of the system being optimized grows (in theory). Your software can take into account lots of machines, learning from a much larger training set and then optimizing both within the machine and for the group of machines working together. Think of a wind farm. There are certain optimizations you need to make at the machine level: the turbine turns itself to face into the wind, the blades adjust themselves through every cycle in order to account for flex and compression, and the machine shuts down during periods of dangerously high wind.

System-wide optimization means that when you can operate each turbine in a way that minimizes air disruption to other turbines (these things create wake, just like an airplane, that can disrupt the operation of nearby turbines). When you need to increase or decrease power output across the whole farm, you can do it across lots of machines in a way that minimizes wear (i.e., curtail each machine by 5% or cut off 5% of your machines, or something in between depending on differential output and the impact of different speeds on machine wear). And by gathering data from thousands of machines, you can develop highly-detailed optimization plans.

By tying machines together, the industrial Internet will encourage “platformization.” Cars have several control systems, and until very recently they’ve been linked by point-to-point connections: when you move the windshield-wiper lever, it actuates a switch that’s connected to a small PLC that operates the windshield wipers. The brake pedal is part of the chassis-control system, and it’s connected by cable or hydraulics to the brake pads, with an electronic assist somewhere in the middle. The navigation system and radio are part of the same telematics platform, but that platform is not linked to, say, the steering wheel.

The car as enabled by the industrial Internet will be a platform — a bus, in the computing sense — built by the car manufacturer, with other systems communicating with each other through the platform. The brake pedal is an actuator that sends a “brake” signal to the car’s brake controller. The navigation system is able to operate the steering wheel and has access to the same brake controller. Some of these systems will be driven by third-party-built apps that sit on top of the platform.

This will take some time to happen in cars because it takes 10 or 15 years to renew the American auto fleet, because cars are maintained by a vast network of independent mechanics that need change to happen slowly, and because car development works incrementally.

But it’s already happening in commercial aircraft, which often come from clean-sheet designs (as with the Boeing 787 and Airbus A350), and which are maintained under very different circumstances than passenger cars. In Bombardier’s forthcoming C-series midsize jet, for instance, the jet engines do nothing but propel the plane and generate electricity (they don’t generate hydraulic pressure or compress air for the cabin; these are handled by electrically-powered compressors). The plane acts as a giant hardware platform on which all sorts of other systems sit: the landing-gear switch communicates with the landing gear through the aircraft’s bus, rather than by direct connection to the landing gear’s PLC.

The security implications of this sort of integration — in contrast to effectively air-gapped isolation of systems — are obvious. The industrial Internet will need its own specially-developed security mechanisms, which I’ll look into in another post.

The industrial Internet makes it much easier to deploy and harvest data from sensors, which goes back to the system-wide intelligence point above. If you’re operating a wind farm, it’s useful to have wind-speed sensors distributed across the country in order to predict and anticipate wind speeds and directions. And because you’re operating machine-learning algorithms at the system-wide level, you’re able to work large-scale sensor datasets into your system-wide optimization.

That, in turn, will help the industrial Internet take in previously-uncaptured data that’s made newly useful. Venkatesh Prasad, from Ford, pointed out to me that the windshield wipers in your car are a sort of human-actuated rain API. When you turn on your wipers, you’re acting as a sensor — you see water on your windshield, in a quantity sufficient to cause you to want your wipers on, and you set your wipers to a level that’s appropriate to the amount of water on your windshield.

In isolation, all you’re doing is turning on your windshield wipers. But if your car is networked, then it can send a signal to a cloud-based rain-detection service that geocorrelates your car with nearby cars whose wipers are on and makes an assumption about the presence of rain in the area and its intensity. That service could then turn on wipers in other cars nearby or do more sophisticated things — anything from turning on their headlights to adjusting the assumptions that self-driving cars make about road adhesion.

This is an evolving conversation, and I want to hear from readers. What should be included in the definition of the industrial Internet? What examples define, for you, the boundaries of the field?


This is a post in our industrial Internet series, an ongoing exploration of big machines and big data. The series is produced as part of a collaboration between O’Reilly and GE.

Related

December 18 2012

Interoperating the industrial Internet

One of the most interesting points made in GE’s “Unleashing the Industrial Internet” event was GE CEO Jeff Immelt’s statement that only 10% of the value of Internet-enabled products is in the connectivity layer; the remaining 90% is in the applications that are built on top of that layer. These applications enable decision support, the optimization of large scale systems (systems “above the level of a single device,” to use Tim O’Reilly’s phrase), and empower consumers.

Given the jet engine that was sitting on stage, it’s worth seeing how far these ideas can be pushed. Optimizing a jet engine is no small deal; Immelt said that the engine gained an extra 5-10% efficiency through software, and that adds up to real money. The next stage is optimizing the entire aircraft; that’s certainly something GE and its business partners are looking into. But we can push even harder: optimize the entire airport (don’t you hate it when you’re stuck on a jet waiting for one of those trucks to push you back from the gate?). Optimize the entire air traffic system across the worldwide network of airports. This is where we’ll find the real gains in productivity and efficiency.

So it’s worth asking about the preconditions for those kinds of gains. It’s not computational power; when you come right down to it, there aren’t that many airports, aren’t that many flights in the air at one time. There are something like 10,000 flights in the air at one time, worldwide; and in these days of big data, and big distributed systems, that’s not a terribly large number. It’s not our ability to write software; there would certainly be some tough problems to solve, but certainly nothing as difficult as, say, searching the entire web and returning results in under a second.

But there is one important prerequisite for software that runs above the level of a single machine, and that’s interoperability. That’s something the inventors of the Internet understood early on; nothing became a standard unless at least two independent, interoperable implementations existed. The Interop conference didn’t start as a trade show, it started as a technical exercise where everyone brought their experimental hardware and software and worked on it until it played well together.

If we’re going to build useful applications on top of the industrial Internet, we must ensure, from the start, that the components we’re talking about interoperate. It’s not just a matter of putting HTTP everywhere. Devices need common, interoperable data representations. And that problem can’t be solved just by invoking XML: several years of sad experience has proven that it’s certainly possible to be proprietary under the aegis of “open” XML standards.

It’s a hard problem, in part because it’s not simply technical. It’s also a problem of business culture, and the desire to extract as much monetary value from your particular system as possible. We see the consumer Internet devolving into a set of walled gardens, with interoperable protocols but license agreements that prevent you from moving data from one garden into another. Can the industrial Internet do better? It takes a leap of faith to imagine manufacturers of industrial equipment practicing interoperability, at least in part because so many manufacturers have already developed their own protocols and data representations in isolation. But that’s what our situation demands. Should a GE jet engine interoperate with a jet engine from Pratt and Whitney? What would that mean, what efficiencies in maintenance and operations would that entail? I’m sure that any airline would love a single dashboard that would show the status of all its equipment, regardless of vendor. Should a Boeing aircraft interoperate with Airbus and Bombardier in a system to exchange in-flight data about weather and other conditions? What if their flight computers were in constant communication with each other? What would that enable? Leaving aviation briefly: self-driving cars have the potential to be much safer than human-driven cars; but they become astronomically safer if your Toyota can exchange data directly with the BMW coming in the opposite direction. (“Oh, you intend to turn left here? Your turn signal is out, by the way.”)

Extracting as much value as possible from a walled garden is false optimization. It may lead you to a local maximum in profitability, but it leaves the biggest gains, the 90% that Immelt talked about in his keynote, behind. Tim O’Reilly has talked about the “clothesline paradox“: if you dry your clothes on a clothesline, the money you save doesn’t disappear from the economy, even though it disappears from the electric company’s bottom line. The economics of walled gardens is the clothesline paradox’s evil twin. Building a walled garden may increase local profitability, but prevents larger gains, Immelt’s 90% gains in productivity, from existing. They never reach the economy.

Can the industrial Internet succeed in breaking down walled gardens, whether they arise from business culture, legacy technology, or some other source? That’s a hard problem. But it’s the problem the industrial Internet must solve if it is to succeed.


This is a post in our industrial Internet series, an ongoing exploration of big machines and big data. The series is produced as part of a collaboration between O’Reilly and GE.

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