From the Editors of CONTROL
Matrikon’s Mik Marvan provided his take on alarm philosophy from the Best Practices Track in a speech entitled, "Innovation: Predicting Maintenance in Hydroelectric Power Generation," during the Matrikon Summit 2007 in Chicago. In addition, Neil Gregory from Meridian Energy gave a follow-up to yesterday’s general session discussion by Garth Dibley. Meridian Energy is NZ largest energy provider. They are a “renewable” company, generating only from hydro and wind. They have huge hydro assets and operate the largest wind farm in NZ’s North Island.
Asset Management Excellence
We are trying to maximize the lifecycle and the long term asset value of the large equipment assets. 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.
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 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. 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 overall 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.”
Meridian uses the PAM to predict things like “days to transformer dry out” and other Key Maintenance Indicators. Managers can access maintenance summary screens that allow drill-down into Maximo to see work order status. They can also see alert summaries, so they can keep track of the “hot” items they’re responsible for. The PAM can email reports to key people so they can be reminded rather than expecting them to find the right screen every day.
Gregory showed the “custom dashboard builder” and called it one of his favorite pieces of the software. Plant managers and engineers can build their own dashboards to keep track of assets they are worried about.
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.
In the future, we expect to have twice as many assets in the model by 2009.
Best Practices: How to Stop Drowning in a Sea of Alarms
This morning’s final session in the “Best Practices” track focused on alarm management. Dr. Joseph Alford, recently retired from Eli Lilly, and Mik Marvan, Matrikon’s alarm management product manager, threw a life preserver to automation professionals drowning in a tsunami of alarms. Dr. Alford condensed 35 years of batch processing alarm management experience into a few key slides.
To rescue the average batch processing plant floor operator from the more than 3,000 alarms he has to account for on a given shift, do the following things, says Dr. Alford
Using the “alarm management” system on a car as an example, he demonstrated the characteristics of an ideal alarming system. In a car, the “alarms,” such as a low fuel gauge, represent an “abnormal” situation that requires a response. The alarm systems are accurate and reliable. Ordinarily people don’t question whether the low fuel indicator really means they have to stop for gas. These alarms permit a reasonable time for response: The driver has time to get to the gas station. Most important, the alarms are few in number, in spite of the fact that cars are complex systems. Why can’t the alarm systems in our batch processing operations have the same characteristics, asks Dr. Alford.
Allowing for the additional characteristics of batch operations, such as multiple process steps, multiple phases, process loads, set points that are a function of time, and few, if any steady-state operations, these are his recommendations for building such a system.