With the increase in automation, sensors, and data in the factory, the question of what to do with all the data that’s being collected is being asked more and more. The first step for any manufacturer is to manage all of the data being collected.
GE has been dealing with Big Data for quite some time, Walsh says, with more than 200,000 GE assets connected around the world. How does Big Data get big? Consider just one consumer goods manufacturer, which takes 152,000 sensor samples every second. “That adds up to 4 trillion samples a year that they’ve got to figure out a way to deal with,” Walsh notes.
But it’s not just about the size of the data; it’s about the type as well, Walsh says. With 4 trillion data points, you need to be able to correlate that data, managing it in a consistent and coherent way.
Walsh emphasizes the importance of laying a solid foundation, however, and not trying to rush the data process. “Everybody wants to start too far up the continuum,” he says. “They want the change-the-world analytics. But you need to lay the foundation. That’s not as sexy as the analytics that have an impact on your balance sheets.” He adds, however, “If you have the foundation set up, your ability to accelerate up that value continuum increases exponentially.”
In addition to managing the data, however, getting people to begin to accept the data is a large hurdle.
The technology of Big Data isn’t actually the hard part; it’s the people, Courtney says. To a large degree, there is a shift in thinking that needs to happen for Big Data to really be effective. An operator often knows just by the vibration or sound that a machine is going to go down. And now we’re asking that operator to trust numbers instead of his own instincts. “If you can’t get him to take action, predictive analytics is useless.”
Courtney cites an example from a recent pilot with a potential customer. GE’s sensor data found vibration on a turbine that didn’t make sense. So GE asked the manufacturer to shut it down to take a look. The operator, however, insisted there was nothing wrong, and didn’t want to stop production for what he saw as load-related vibration. Some time went by, and GE was still monitoring the disturbing vibration on what was a very expensive asset. So they contacted the company again, urging them to shut it down.
Because it was a trial situation, the customer essentially said that if they shut it down and GE was wrong, that would be it for their relationship. But GE stood by its data, confident that there was a problem that needed to be explored. The customer shut down the turbine and put a bore scope in, finding corrosion on one of the blades that ran most of the way through. As Chad Stoecker, who runs GE’s Industrial Performance and Reliability Center, put it, “That blade was three to five days from liberation.”
While this issue was a large one that could have cost the company millions, more often than not, the data being analyzed is catching problems before they reach this point. The idea is to be proactive, finding small problems and fixing them before they become large, costly problems. In manufacturing, where uptime is the goal, finding and fixing the problem before it happens is the name of the game. And Big Data is part of the solution.
Read more about Big Data at Automation World.