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Moving Big Data to Equipment Design

Moving Big Data to Equipment Design

Using computer intelligence for diagnostics and prognostics in industrial equipment has been in play for at least a decade. As data processing becomes less expensive and easier to manage, more can be done to fine-tune equipment and predict maintenance problems before they occur. Now, big data is getting deployed in the design process of the equipment, so operations data can get incorporated into the next generation of the equipment.

For example, GE monitors data from jet engines to ensure optimal performance and maintenance of the asset. That same data is also critical to engineers working on the next generation of engines. "Real-time performance data is helping engineers to better understand how the engines are operating and how they are being used. This is invaluable information for design teams," Bernie Anger, general manager of GE Intelligent Platforms, told Design News. "This isn't only true of aerospace assets. This data-driven design approach is happening throughout the industrial sector."

MORE FROM DESIGN NEWS: Learning to Use Big Data

Big data isn't new to GE. The company has used big number crunching to optimize operations for years. "We started gathering data onsite to monitor our machines 15 years ago. We moved that data with Internet connectivity 15 years ago," Anger told us. "What we started to do recently is to use the data to design control systems for those machines so they can operate closer to their limitations in a safe way."

Anger uses the wind turbine as an example. If the pitch of the turbine is too high, the turbine can be damaged by dust storms. However, if the control system can detect and react to dust as it appears, the turbines can run at an accelerated pitch, thus improving performance. "You adjust pitch so when dust comes it doesn't destroy the equipment. We've been able to build automation that gets closer to the physical limits," he said.

The goal is to squeeze additional performance from existing equipment. "We can utilize the same materials and get more performance out of it," said Anger. "We were able to convert a 1 megawatt turbine into a 1.6 megawatt turbine without any changes in material. We're doing that across a lot of mechanical devices."

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Anger insists that the design team needs to incorporate operations data into the design decisions. "Are you using the data you're collecting to learn how your machines are performing? If you sell machines in multiple units, do you have the opportunity to reevaluate the machine?" he asked. Anger noted that it doesn't take a radical improvement in equipment design to justify re-evaluation based on operations data. "When we talk about the impact of improvement, we're talking about the impact of 1%," he said. "If you can get 1% of improvement, you can get 1% increase in profits."

He points to the wind farm as an example. The wind farm needs data to optimize beyond the performance of a single turbine. "We're looking at data across machines and thinking about the farm as a unit of control," he said. "Then we're using data to optimize the design of the turbines based on the performance of the farm. In the past you organized the individual machines."

Rob Spiegel has covered automation and control for 15 years, 12 of them for Design News. Other topics he has covered include supply chain technology, alternative energy, and cyber security. For 10 years he was owner and publisher of the food magazine, Chile Pepper

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