Grasp Maintenance’s Bottom Line

Effective predictive maintenance demands more than technology.

By C. Kenna Amos, contributing editor

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Too many chemical companies still undervalue the role of maintenance. “Most CEOs don’t care about how much you do in maintenance, until you cannot deliver — and then they blame you,” says Jay Lee, director of the National Science Foundation’s Center for Intelligent Maintenance, at the University of Cincinnati, Cincinnati, Ohio. Perhaps that’s why he champions precision maintenance, which details how much to do, where and what’s derived from adjustments.

Predictive maintenance (PdM), which focuses on equipment health, plays a key role, he notes. However, this should include more than vibration analyses or condition monitoring — both of which he labels as problem-identifiers. “When you are problem-centric, you don’t forecast — and predictive maintenance is forecasting,” Lee stresses. To him, forecasting means collecting daily equipment metrics for use in plotting deterioration in equipment performance. “With degradation rates — the health map of a machine — you can prioritize maintenance schedules.”

Prioritizing those schedules starts with technologies and people, including non-maintenance staff. For example, operators can do inspection rounds to spot trends in equipment performance, notes Scott Brady, manager of marketing and operator-driven reliability for SKF Condition Monitoring, San Diego, Calif. “And if a trend becomes high, the maintenance guys get notified.”

However, getting information presumes you know what to do with it. “How do you convert data to useful health of equipment?” Lee asks. “[And] how do you prioritize and optimize actions?” For example, if a machine’s performance is degrading, do you repair immediately or examine the impact of failure — and base the decision on the change’s importance and when it’s made? He also asks, “How can you physically convert data into information that can be used by everybody?”

Safety First
Lee believes chemical plants must become safety-centric regarding processes and equipment. That involves comparing data and their frequency to the impact on safety but also looking at costs separately. Superimposing all those data, end-users can identify “the criticality line of safety over cost. As you approach this criticality line, you may need to take more aggressive action,” he says. “You may have to do more than just check but stop operation and do a detailed examination.”

Concentrate on assets, especially those related to safety, having the highest criticality, suggests Bart Winters, solution manager for safety and reliability with Honeywell Process Solutions, Phoenix, Ariz. “It [criticality] can be measured by lost production or from a safety standpoint but safety definitely should take priority.” What’s inappropriate, though, is what he calls the “peanut butter approach,” that is, spreading the effort equally across all assets. Instead, he recommends evaluating where spending has the most impact. This best-bang-for-the-bucks viewpoint pays off. At a typical chemical site, a 1% improvement in overall equipment effectiveness “could be about $1 million annually in increased profit,” he says.

Oxea Chemical Co., Bay City, Texas, clearly rejects the peanut butter approach. Its site has approximately 500 pieces of equipment, including compressors, lots of ANSI- and a few API-style pumps, and even some exotic custom-modified machines. Oxea has contracted with Rockwell Automation to regularly collect and process data on this equipment.

“Some equipment is critical and not spared [so it’s] very important to keep it running,” explains reliability engineer Don Kincer. Some products are sold out, so “you have to keep interruptions minimized,” he adds.

Using Enpac portable condition-monitoring data collectors and signal analyzers, Joey Gramoski, a Rockwell field-services engineer who visits on a preset schedule, provides an exception report that Kincer and rotating-equipment specialist Robert Slaton review. It details what’s becoming unique or abnormal “like a discrete frequency or vibration that was not there before,” notes Kincer, adding: “Most of the time, we’ll go out to the machine he [Gramoski] pointed out and make a maintenance decision shortly after we get his report.”

This new PdM-focused, boots-in-the-field approach to maintenance substantially altered Oxea’s financials. “When I first got here, it was preventive maintenance for bearings. Every year, we replaced all bearings, whether they needed to be or not,” Slaton recalls. But now with the new vibration-focused PdM program, “we’ve removed the work orders and replaced them with the new program. Now, we may be replacing bearings every five years — and it could even be longer.”

Oxea no longer can afford the old way of maintenance, stresses Kincer. “We’re keenly aware of plant behaviors, instead of carte blanche replacing things. We want to know why things broke.” For good reason, too. The plant’s maintenance budget was in the $10-million range, but “now we’re at about $7 million and trending down,” he notes. That’s possible because, as Slaton says, “we’re going to ask equipment how it feels” and have planned versus unforeseen interruptions.

Smarter Monitoring
Oxea’s experience emphasizes how much companies need some intelligent information or mechanism to understand conditions.

Rotating equipment is a good case in point, notes Winters. “With some onset of a problem such as [with] a bearing, several things can be picked up before a failure occurs to prevent secondary or collateral damage.” For instance, vibration data may show a problem “typically months or weeks before failure occurs.” Then there’s also oil analysis and thermography, he adds.

Embedded systems in instrumentation can provide early event detection (EED) and advanced diagnostics for problems such as bearing deteriora-tion and cavitation in pumps, notes Ravi Kant, Chanhassen, Minn., based manager of global refining and petrochemicals marketing for Rosemount Measurement, a part of Emerson Process Management. Depending upon the issue, intelligence can be derived from first principles or operating data.

Rosemount now is running field tests of high-speed sensors (22-Hz data collection) for EED. Trials include for detecting: flooding and damage to internals in distillation columns; flame instability and burner problems in fired heaters; catalyst transfer issues in fluid catalytic cracking units; fouling in heat-exchangers; and presence of wet gas in compressor suction lines.

The Abnormal Situation Management Consortium, Phoenix, Ariz., cites several situations in which early information has provided significant benefits. In one case a frozen differential pressure cell that could have led to column flooding was detected six hours before the first alarm would have been triggered, saving a plant from a major upset and a large financial hit.

Indeed, EED’s value isn’t only in warning of developing problems but in helping track down their root causes such as failed sensors, plugged lines and sticking valves, adds Steve Johansen, global services marketing manager for advanced solutions at Honeywell.

“The multivariate statistical analysis and principal component analysis methods within Honeywell’s EED toolkit are generic in the sense that they can be used to analyze and correlate very small (a single piece of equipment) to very large (an entire process) data sets,” he says.

Process-oriented technology can detect oscillation in pumps, notes Moin Shaikh, marketing manager for process control systems at Siemens Energy & Automation, Spring House, Pa. It looks at variation in pump out-flow, assessing whether valves are performing optimally or not. “Based on that, you can set predictive-maintenance conditions to schedule mainte-nance,” Shaikh adds.

Sometimes, though, it’s necessary to have smart instrumentation that trips an alarm to alert operators that a certain piece of equipment has reached a critical maintenance level, he notes.

“Online remote diagnosis with real-time data and data validation — with efficient logic analysis tools and effective work processes — are keys for successful predictive-maintenance program at any site,” says Amit Ajmeri, consultant for asset-management solutions with Yokogawa Corpora-tion of America, Sugar Land, Texas.

Eyes in the Field
But people will continue to play a crucial role. “You need them to see the obvious things,” Kant says. Honeywell agrees. Its chemical plants (within Honeywell Specialty Materials) employ maintenance-excellence programs that rely on operator field surveillance. Checking pumps for noise may alert operators to a problem “normally days, if not weeks, before failure,” notes Winters.

Brady advocates operator-driven reliability (ODR), a preventive/predictive maintenance mix that focuses on having reliable eyes in the field. “During shifts, the operations guys have always done rounds with check sheets.” Now, though, devices such as industrial personal digital assistants (PDA) allow data “to get into a database people can view. If a trend becomes high, the maintenance guys get notified,” he says.

While operators are making visual inspections they also can generate, not just record, data, e.g., on vibration, thanks to wireless sensors (Figure 1). Brady says such devices will assume an ever-increasing role primarily because of their reliability and safety, especially with hazardous environments.

Figure 1
Tall order no longer. Wireless sensor means that equipment location doesn’t hamper monitoring.
Source: SKF


Regardless, “it’s good for operators to be out there. It’s seeing the machine, hearing the machine, smelling the machine,” Brady adds. “Nobody can replace a person.”

This role for operators makes the need for cooperation with maintenance all the more important, Kant emphasizes. “Because of a lot of these advanced-monitoring techniques, it’s the operating people who get the first warning or signal. Therefore, they need to call maintenance right away.” Kincer warns, “If operations and maintenance aren’t talking, then there are problems.”

Pay attention to another people issue — staff expertise, counsels Johansen. With people who are really knowledgeable about specific equipment increasingly scarce at sites, it may make sense to bring in an authority to help with some PdM issues.

Based on Honeywell’s experiences and those of its end-users, Winters lists three criteria for excellent maintenance programs. First, get the basics right and use the right technology. He suggests a good field-surveillance program with mobile hand-held devices — Honeywell uses its own Intela-Trac PKS devices at its facilities — to collect information about an asset so those data can be trended.

Next, monitor other relevant parameters such as vibration, lubricant condition and temperatures that can be sensed through thermography. “Typically, these technologies can be used to predict failures,” he says. Consider how wireless technology can help, he adds. “We recently completed a project on an oil tanker. We put wireless infrastructure inside the ship to monitor the pumps.”

Finally, adopt technologies that give data useful for first-principles or empirical EED modeling. “That’s a little bit more specialized. It’s not employed as broadly as the others. It’s a technology we use in certain instances to monitor important events.” For example, at one of its chemical plants, Honeywell uses an IntelaTrac device with EED, he says.

The Culture Question
The availability of field-intelligence technologies and the quest to minimize shutdowns and downtime certainly are strong drivers for PdM, says Kant. However, he adds, success also depends upon top-management buy-in, proper motivation for excellence and company culture. “Culture is where the actual execution of predictive maintenance takes place. It is a change of thinking.” All are needed, he stresses.

“When top management deployed such PdM strategy, but did not have full support from maintenance as well as not having the strategy implemented as part of the work process, that had a high probability of failure,” cautions Ajmeri.

Brady emphasizes that besides technology to enable a successful program and a process to get it done, you need right attitudes to make the other two click (Figure 2). “Where do we see things fail? Where the culture’s not there. Unless the culture’s there, it [the maintenance program] doesn’t go anywhere.”

Figure 2
Necessary Elements: Success with a maintenance program requires proper tools, attitude and procedures.
Source: SKF


This demands a change in thinking about maintenance, says Lee. “We have to elevate the image of maintenance systems.” Deafness by top management no longer works — and shouldn’t, he stresses. After all, as Kincer states, “Predictive maintenance is a matter of competitiveness.” Indeed, notes Kant, top management that does buy into PdM sees its real value: “They love it because it goes to reliability and, thus, the bottom line.”

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