"We are trying to maximize the lifecycle and the long term asset value of the large equipment assets," Gregory said. "We are trying to optimize our maintenance. At a strategic level we're talking about refurbishment and replacement of aging assets. With aging assets it is all about when you do that. We have tools to help us understand the plant equipment condition. But it is harder to predict failure from condition monitoring."You can replace before failure. This is a low risk option, but it is very expensive. The strength of the Predictive Asset Management system is that it works for all levels of equipment from large to small assets. You can replace after failure. This, too, is a suboptimal choice. Emergency replacement can cost as much as five times normal purchase cycle cost, not to mention the potential environmental damage. "We have transformers in a World Heritage site," Gregory said, "and chucking 15,000 liters of transformer oil down the ridge just isn't right." Plant condition monitoring: "You must understand the condition of your asset," Gregory asserted, "or you're just a victim waiting for an accident to happen." But if you do understand conditions, you can make informed decisions about maintenance and repair. The problem with our first condition monitoring system was that we were flooded with data. "The problem with data is that you have to finally get people to look at it. We just didn't think it was good enough to get someone somewhere to spend time looking at the data." We thought we needed a Plant Asset Management System. Gregory noted, "What is different about a PAMS is that it is designed to do something with the data from the condition monitors, not just collect the data." We believed that we needed a predictive PAMS, and we found in 2003 that there was absolutely no such system on the market. We specified what we wanted, and found that Matrikon was successful in obtaining the project. "There was actually daylight between Matrikon and the next best company. And Matrikon was the only one of the six companies on the short list I'd never heard of."
We had 10 years of CMMS data from Maximo, 3 years of PI data, and lots of process data. What we didn't have was the bit that pulled it all together. That's the PAMS.
The PAMS analyzes the data from Maximo and the PI database, and notifies the right people of the results of the analysis. Then somebody can actually do something about the data. Gregory showed asset overview slides that gave overal health values for critical assets like transformers and turbines. "We needed an icon for "˜governors' so we picked a photo of the Governator, since the PAMS identifies bad actors as well, eh."
"We don't expect this system to warn us of catastrophe everyday. What we expect is an early warning system. The system is like the barrier at the top of the cliff that keeps you from going off. It is the light on the dashboard that tells you that something is coming and you need to do something about it," Gregory said.In the future, we expect to have twice as many assets in the model by 2009.