Executives are gaining new knowledge of and respect for good maintenance practices. Years of cost- and staff-cutting and attrition have taken their toll on equipment reliability. ERP systems are red-flagging asset availability and costs of lost production as reasons why executive bonuses are flat. The best are coming to their plant engineering, maintenance and reliability departments looking for answers.
But they’re not willing to spend more on labor. Benchmark studies show them their maintenance dollars are already excessive. In their opinion, costs are too high and they’re not getting enough return on investment.
Those same studies tell them that predictive maintenance is high on the list of best practices. They know something about predictive maintenance, and now they want to hear about how industrial-strength applications can make their plants run both more cheaply and reliably.
Management sees the light
Back in the boom years of the 1990s, while many other industrial facilities were allowed to dabble in complacency, deregulation and privatization, management was putting pressure on electric-power-generating facilities to significantly increase reliability and efficiency. “Ten years ago, we had a very strong preventive and corrective program,” says Roger Cole, reliability-based maintenance coordinator for AEP’s John Amos Power Plant in Big Scary, W.Va. (formerly known as the Big Scary Power Plant). “We had some predictive, but we didn’t leverage it. We had twice the workforce, so we were able to do a lot of testing.”
Then the utility industry began to deregulate and cut costs. “We went through a period of shrinking workforce: When people left or retired, we didn’t replace them.” Cole says. “We got to the point where we didn’t have enough manpower to keep the plant running reliably, to avoid catastrophic failures and manage the workload. We were in a run-to-failure mode that became very expensive due to equipment damage and load curtailments.”
In many industries today, efficiency has been sacrificed in the name of cost-cutting. “Maintenance saves $10, but it costs operations $20,” says Neil Cooper, general manager for Invensys Avantis. “It’s not that the technologies aren’t there. The problem is corporate hasn’t seen the proper role of the plant, and plant managers have not seen the big picture, so they drive maintenance costs down separately from trying to improve availability and output. Before we make maintenance decisions we have to weigh them in the context of corporate goals.”
There hasn’t been an awareness at senior and corporate levels, but there is a change in progress -- a dawning realization that maintenance is not a necessary evil, it’s a significant contributor to the upside (Figure 1).
From what our practitioner, expert and vendor sources tell us, we can see that there’s a tsunami of predictive technology unfolding as we write. Read on and you’ll be ready to talk with management about why now is the time to invest in more effective asset management. Simply put, it costs but it pays, and you need it to compete.
Figure 1. Effective maintenance practices boost the bottom line by improving operating equipment effectiveness (OEE), reducing the cost of goods sold and avoiding capital expense.
From high-resolution, easy-to-use infrared cameras you can actually afford to sophisticated vibration algorithms to oil analysis services that deliver timely reports you can put into action, there’s no shortage of practical predictive technologies. There is also a well-defined hierarchy of maintenance practices (Figure 2). The problems, as usual, are in implementation.
Nowhere is this more obvious than with the engineers’ favorite steamroller, reliability-centered maintenance (RCM). Do a thorough root cause failure analysis, evaluate the risks, perform the prescribed preventive procedures and predictive inspections, and rock-solid reliability will surely follow.
But it costs too much. “Proper RCM as defined by the original gurus is too expensive,” Cooper says. “It’s not practical. You have to do the 80/20 rule.”
The essence of streamlined RCM (SRCM) is that 20% of the input gives 80% of the results. “SRCM is more cost-effective because it helps you focus more effort on critical failure modes,” says Scott Brady, general manager of decision support systems for SKF. “Whereas regular RCM makes you look at all failure modes, only to find that 70% are non-critical and not important,” he says.
You don’t have to know everything about every asset. “We don’t analyze what happens if a washer fails on a pump foot, we look at what happens if the pump fails and determine the most common failure modes,” Brady says. “We identify critical systems, look at common failure modes, and put tasks in place to predict and prevent those failures. We gather what we need to know to keep the asset working, and use decision support to drive the data to decisions.”
Use multiple technologies
Determining, monitoring and diagnosing the most common failure modes calls for multiple condition-monitoring techniques. Back at the John Amos Power Plant, technologies are now combined to detect and zero in on causes of incipient failures. “We use several technologies,” Cole says. “Infrared, vibration, oil analysis -- no one technology can do it alone. We might see a vibration, then take oil samples to help pinpoint the cause. Vibration doesn’t solve all the problems. It’s excellent for antifriction bearings and gears, for example, but not so good for sleeve bearings. And we’ve made our major finds with infrared.”
Cole describes a 5,000 hp motor where the bearing failed on the inboard side. “It was so bad the rotor dropped into the stator, but the motor RTDs were all on the outboard side, so the motor temperature readings looked normal,” he says. “Infrared showed the inboard side was tremendously hot.” The plant does all its motor rebuilds, and now puts RTDs on the inboard side as well.