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“We’re concerned mainly about failed bearings and failed gears,” says Matt Morris, North Star BlueScope’s reliability-team leader. “All of the gearing is unique to the machine, and we have no spares. Because getting a new one usually takes at least six months, limping along or shutting down completely can add up to large losses. However, the field of available methods is very narrow because many of our large bearings turn very slowly. Also, the sensors mounted on each bearing have to withstand the high temperatures and heavy vibration in our processes.
“Initially, the SWAN system was standalone, but it was eventually added to our Level 1 network for backup purposes. Now, it’s also accessible through routers from our main Level 3 business systems.”
Not only has access to this information reduced production losses, but it also has allowed NorthStar to shrink its $3 million inventory of spare parts, and avoid paying thousands of dollars for rush orders.
Networking, Education Needed
While machine health monitoring technologies have made technical advances on their own, they’ve also been aided by increasingly reliable and widespread networking solutions and protocols. Breeding says developers had been trying to integrate process data for years, but that previous networks weren’t durable and secure enough. Now, not only are hardwiring and connectors more robust and less expensive, but fieldbus protocols, such as OPC, Profibus, Foundation fieldbus, and Modbus are becoming more standardized and accepted by end users. Likewise, many former worries about network security have been lessened by the fact that many process industry companies have established virtual private networks (VPNs), and are growing more comfortable about allowing authorized access to them. Also, once any initial network connection is made, it’s possible to distribute machine health monitoring data up to the enterprise level, over wireless links, or via the Internet.
Though many increasingly sophisticated technologies are coming into play in machine health monitoring, none can be effective if they simply aren’t used. “Many customers think their job is done when they buy a machine health product. Our company found that many users had very nice monitoring technologies suffering from ‘dusty keyboard syndrome” because they were still using schedules and weren’t relying on condition-based monitoring in their daily routine,” says Steve Sabin, editor of GE Energy’s Orbit magazine. “Though it started out making instruments, this was why Bentley Nevada started its machine diagnostic services business. Having monitoring instruments was fine, but companies that got the best results were those that trusted their instruments to drive their behavior, and change they way they did their work. In the best plants, 80% of the maintenance work is planned, while 20% is reactive, but this still isn’t they way most plant are run.”
So, while machine health monitoring is still solidly based on empirical observation, the power of that observation, the focused analysis of the resulting data, and the faster and wider distribution of its results can give user unprecedented benefits, but only if they use them.
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