It’s maybe not the most conventional trajectory for asset performance management: Salt River Project (SRP), a Tempe, AZ-based public power utility (also the oldest federal reclamation project in the United States), for years relied on an outside partner to handle the heavy lifting when it came to its predictive data analytics. And then, in 2012, with the support of said outside partner, it brought that technically demanding work back in-house.
The back story: SRP began working with GE Digital’s Managed Services team (formerly SmartSignal) way back in 2005 after recognizing that with better use of asset performance data, it could begin to shift out of reactive maintenance mode.
“We had a lot of data coming in from our coal and gas plants, and we were doing mostly kind of post-mortem (analysis),” says Andy Johnson, engineering supervisor for power generation services at SRP. “After something occurred with a specific piece of equipment, we would go back into that data and try to identify what were the causes of those issues.” In learning more about the emerging field of predictive analytics, Johnson says, SRP saw “that having this data was a very valuable resource, but...we weren’t doing enough with it.”
SRP worked with GE to deploy a predictive analytics software program, GE’s SmartSignal, at a single pilot site. The utility already had several years’ worth of asset data from the site; this data was built into predictive analytics models that SRP was eager to use as “an early warning system of potential issues” with the equipment it was using, Johnson says. “As a side benefit,” he adds, “we were also able to begin moving from kind of calendar-based maintenance to more condition-based maintenance activity.”
GE Digital itself was building out and fine-tuning the software as early adopters such as SRP were using it – the relationship was collaborative from the beginning, say Johnson and GE Digital’s Chad Stoecker, who was involved with the implementation. “We got this very early preview that gave us an early view of what we could do and how we could take it fleetwide,” Johnson says. Adds Stoecker: “With all of our customers, we’re always trading ideas back and forth…we’re all trying to go to the same thing, which is to create a safer work environment, a more environmentally efficient work environment, a more profitable industrial work environment.”
In 2012, SRP was ready to expand use of the predictive analytics models across its sites. But before making that move, SRP made a big decision: It decided to pull management of the models in-house, recruiting and training a team of its own performance analysts and engineers to oversee the asset performance management tools and make specific maintenance recommendations to different SRP facilities.
Why? “We thought, you know, by having our own staff looking at these models, maintaining these models...it gives you the opportunity to have that built-in trust factor,” Johnson says. And as any manager charged with overseeing deployment of new technology knows, earning the confidence of workers who will interact with the new technology – and with technical support teams – is no small task.
“Sometimes you worry, are the plants going to trust you? Are they going to see you as Big Brother looking over their shoulder, or are they going to see you as your co-worker, your friend watching your back for you?” Johnson says. “One thing we’ve been very conscious about is building that trust, and by having our own people internally do the monitoring, modeling, and maintenance of the models, we’re able to build that trust and have that built into our center.”
It was a strategic and carefully planned move, and one that was made more easily via the technical and logistical support provided by GE Digital both before and after the responsibility shift was completed, Johnson comments. “They didn’t just cut us loose when we began monitoring in-house; they’ve always been a partner to us and provided their expertise when we needed it,” he says.