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IF YOU'VE got your health, then you’ve got everything. Though slightly condescending, this old saw is as true for machines in process applications as it is for the people that operate them. And, in yet another similarity to human healthcare, recent advances in sensing technologies are allowing users to look ever deeper and with greater resolution into their machines and process applications. This and focused handling of the resulting data is allowing many users to find and diagnose potential problems even sooner, and make more intelligent decisions on scheduling maintenance, allocating resources, managing inventory, and scheduling to minimize the cost of maintenance and repairs.
Organizationally, it’s true that machine health monitoring and/or management fits within larger, less tangible concepts, such as condition monitoring, preventive and proactive maintenance, asset and lifecycle management, and even enterprise resource planning. However, machine health comes in far more direct contact with the processes it monitors than any ostensibly higher-level methods, and often helps supply them with data they need to function.
“As users move from straight, time-based or run-to-failure maintenance, they find they need information on the condition of their assets. Next, it’s important to integrate that physical, process data into a condition monitoring platform, which can correlate it, and help measure performance,” says Scott Breeding, product line leader for Bentley Nevada, a division of GE Energy. “This is what gives users an indication that a bearing may be running hot before they can see any signs of wear or damage. For example, an engineer may use process data indicating a vibration to deduce that a centrifugal pump is cavitating because an operator is applying inadequate suction-head pressure, and this can support a decision to improve that process.
“Process data also can be used to write new rules around assets, derive subsequent indicators, and screen data to produce alerts when a problem may be likely to occur. This allows users to focus more closely on the ‘bad actors’ in their applications.”
|FIGURE 1: BAD VIBES|
An amplitude/phase/time (APHT) plot of vibration data from the drive rollers on the Wisaforest mill’s lime kiln shows their motor speeds varying between 991 and 1,083 rpm, and demonstrating a structural resonance at 36 Hz as the kiln moved from low- to higher-speed operation, which required stiffening and strengthening the drives’ supports.
For example, Bently Nevada recently implemented its System 1 software, Trendmaster Pro data acquisition hardware and Dynamic Scanning Modules, and 3500 monitoring system and proximity probes on almost 50 process machines at UPM-Kymmene’s Wisaforest pulp and kraft/sack paper mill in Pietarsaari, Finland, and reportedly has solved 10 separate machinery problems. This project also was part of the mill’s overall installation of a new recovery line to maintain its production of 800,000 air-dried tons per year.
Though most of its machines use rolling-element bearings monitored by accelerometers, the mill’s huge lime kiln uses fluid-film bearings monitored by X-Y proximity probes and accelerometer transducers. To let operators see basic condition information data and let rotating machinery engineers see diagnostic data, the mill’s process control system set up a bi-directional OPC interface, which imports amplitude and alarms from System 1 into the DCS, and exports process variables from the DCS into the System 1 database. The main problems that Bently Nevada helped solve at the mill included:
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