Pulling Together on Asset Management?

Users Are Implementing New Preventive Maintenance, Condition-Based and Optimization Tools in More Settings. Here’s How They Make Asset Management Pay Off—If They Can Get Their Staffs to Use It.

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By Jim Montague, executive editor

Being flooded with unusable data isn’t as bad as being flooded with bituminous oil, water, sand, steam or a combination of all four. But it’s not that much better.

Cliff Pederson, production processes manager at Suncor Energy Services in Calgary, Alberta, says this information tide began to rise at Suncor’s in situ water treatment, hot water and steam generation plant shortly after the company installed its first fieldbus system in 2001. The plant’s hundreds of smart transmitters, smart valve positioners and other intelligent devices began churning out mounds of diagnostic data and delivered it through Foundation fieldbus devices to Emerson Process Management’s Delta V software .

However, this information was still more than Suncor’s operators could initially digest or use to make preventive maintenance decisions. As a result, Pederson says they began seeking ways to screen their data and get it into a more usable form, and they started an asset optimization project two years ago. Suncor and its local Emerson distributor and systems integrator, Spartan Controls, implemented Emerson’s Delta V, Asset Management System (AMS) and Device Manager software to prioritize transmitter diagnostics and other messages in four to six criticality rankings, based on Suncor’s own risk matrix.

North of Fort McMurray, Alberta, Suncor drills pairs of horizontal wells in 1,000-ft deep Athabasca oil sands and uses steam-assisted, gravity-drainage to extract oil in a six-to-12-month period. The first two stages of Suncor’s facility have been built and now produce 45,000 barrels per day of bitumen. Stage 3 is under construction, and Stages 4-6 are scheduled.

“The most critical data is from safety equipment or devices that could create a safety or production incident if they fail,” says Pederson. “To determine these levels, our team defined and set its priorities in Delta V’s Asset Portal software, which then ranks incoming messages and displays them to engineers, maintenance staff and operators. Next, users initiate work orders and send technicians to fix problems. This solution is less reactive, allows more preventive fixes and enables more condition-based maintenance and operations.”

And they all lived happily ever after—at least until the next unresolved snag came up. But more on that later.

Refocusing a Fuzzy Map

Asset management has come a long way from the old days of reactive maintenance and running to failure—though many users continue to operate at this early stage. As it’s moved into use-based, scheduled and preventive mai ntenance and onward into condition-based maintenance, the asset management umbrella has grown to cover a dizzying array of topics and technologies. These include maintenance, operations, life cycle, data collection and processing, networking and enterprise systems.

“Process controls have always been focused on three things: monitoring and controlling their process, products and work environment. Now, they’re joined by a fourth piece, controlling equipment assets, which was previously a maintenance responsibility,” says David Berger, PE, partner at Western Management Consultants in Toronto. “And the condition-based monitoring and predictive maintenance that were always there to some degree become much more important, and use software to help monitoring conditions.” (See Asset Management Assessment sidebar.)

Existing Management Methods

For example, a two-engineer staff at Chevron’s plant in Sweeny, Texas, is using ExperTune’s PlantTriage software to monitor 70 performance parameters in about 2,000 loops. The software pulls data from the plant’s distributed control system (DCS) or its historian using an OPC connection, which can show more performance data than the DCS alone.

“Traditionally, technicians used to do rounds, but there aren’t enough people now, so PCs and software are needed to point them in the right direction,” says George Buckbee, ExperTune’s marketing manager. “PlantTriage also did a lot of the number crunching needed to help improve the moisture level in Sweeny’s products and stabilize its overall process. Doing this incorrectly could have costs the plant $10,000 per day.”

Likewise, fertilizer manufacturer CF Industries recently used Invensys Avantis Pro and DSS software to update the structured maintenance program at its plant in Donaldsonville, La. This program collects MRO inventory, procurement and maintenance activities. The new system automates maintenance planning and tracking of 50,000 assets and 60,000 inventory items, including vessels, pumps, rotating equipment and motors. “Avantis DSS takes data from available Microsoft documents, such as Excel, and makes useful information out of it,” says Dave Wiedenfeld, CF’s IT group project. “Data analysis that used to take two weeks can now be done in 10 minutes.” He adds that improved asset management allowed CF to reduce its inventory by several million dollars and saved it about $2 million via improved sourcing and contract negotiations.

“It’s important to understand that every asset has a state that it’s in, but now intelligent devices and their software can give us much more information about them,” says Neil Cooper, general manager of Invensys Process Systems’ manufacturing and business operations management division. “For instance, an intelligent valve can show that it’s 30% open, conduct an automatic partial stroke test or indicate pressures on either side of it. These new data sets can include up to 60 variables, and available condition-management software can aggregate it all, so users can look at whole process control loops, not just individual I/O points. Then, they can put context on this data and find problems that wouldn’t be visible otherwise. For example, they now can apply aggregate rule sets to real-time data and see the set of events that indicate a pump is likely to go bad because these events happened eight or 12 hours before the last one failed. Previously, users did an analysis and set a policy after a failure, but now they can get trends and plan ahead of time.”

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