Machines staying healthy by staying smart

By Jim Montague, Executive Editor

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.

Healthy Evolution 
“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.

Similarly, using screened process data, and performing failure mode of effects analyses (FMEA) can help users pick the most appropriate machine health technology for each asset based on its relative criticality. These typically include trebology (lube oil analysis), vibration analysis, thermography, online and offline motor current monitoring, and several other less well known technologies. “This is more than protecting a machine when it trips. This is showing operators where they’re causing wear or breaks, and preventing trips entirely. And the best users put this information right in front of their operators, and even those that want to keep on operating until failure can do it as an intelligent decision.”

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:

• Identifying intermittent, high-frequency vibration amplitudes in the inboard bearing in the recovery boiler’s 600-kW air fan, which helped the mill’s engineers better schedule lubrication system repairs and bearing replacements.
• Correcting elevated vibration levels in the 15.4 x 443-ft lime kiln’s drive rollers by evaluating their phase, rpm, amplitude and frequency data, which found a structural resonance at 36 Hz as the kiln moved from low- to higher-speed operation, and required stiffening and strengthening the drives’ supports (See Figure 1).
• Locating a cracked inner ring in a bearing in the recovery boiler’s exhaust gas fan, shown by a characteristic “ringing” phenomenon in the amplitude/phase/time (APHT) plot for the fan, which was repaired.
• Identifying excess vibration and a faulty rubber element in a coupling on the recausticizing unit’s white liquor pump, which also was repaired.
• Finding inboard bearing deterioration in the recausticizing unit’s totally unmonitored mixer, which was adjacent to its monitored rotary filter, and required repairing the mixer’s a broken lubrication pipe.