Lean maintenance and predictive technologies

Plant Services’ readers discuss the variables involved in cutting costs

By David Berger, Contributing Editor

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The latest buzzword to hit our shop floors in North America is “lean.” If you have not yet heard of lean thinking (the original concept), lean manufacturing (the most popular term), lean management or lean maintenance, you are not alone. In fact, almost all our focus group participants have not heard of these terms. The goods news is that the notion of “lean” appears to be self-explanatory, given that most focus group participants had an intuitive feel for what the term might mean.

When asked about the lean maintenance concept, they described it as:

  • A means to cut costs
  • Doing more with less
  • Use of preventive maintenance to reduce time spent on maintenance.
  • The minimization of downtime and maximization of efficiency
  • The movement toward a more planned environment 
  • Working smarter

Our focus group discussion then centered on the use of predictive technologies to achieve lean maintenance.

Predictive maintenance

Predictive maintenance is really an extension of preventive maintenance. It is based on the theory that equipment is operating efficiently when measurements of vibration, heat, pressure, tension, speed, alignment and so on fall within an acceptable bandwidth. As the equipment wears, measurements drift beyond established control limits, and preventive maintenance is required to bring it back to optimum operating conditions. Thus, equipment failure can be predicted, so that steps can be taken to prevent production downtime and more costly emergency repair.

In some industries, predictive maintenance can also prevent accidents. In environments where equipment runs 24 hours, seven days a week, a predictive maintenance program is essential. This is because predictive maintenance, unlike many preventive maintenance routines, can be accomplished while equipment is running.

Most focus group participants recognized the key benefits of implementing a predictive maintenance program — reducing costly downtime and improving equipment reliability. Many were doing some level of predictive maintenance, but almost all said they could do a lot more.

Some participants outsourced predictive maintenance services for more specialized equipment such as HVAC. This is because contractors can hire more skilled people who are  trained and focused on leading-edge predictive maintenance techniques. Also, third-party contractors’ economies of scale allow them to purchase sophisticated software, hardware and measurement devices. These factors are what make a predictive maintenance program cost-effective.

The advent of the Internet has further enhanced the benefits of contracting predictive maintenance services. For example, equipment sensors or programmable logic controllers (PLCs) installed on the shop floor can be accessed by third-party predictive maintenance service providers via an online, real-time Internet connection. Alternatively, diagnostic and analysis tools can be accessed remotely via the vendor’s Web site.

Predictive technologies

Predictive maintenance has two components, data collection and analysis. In terms of predictive technologies, many options are available to accomplish these tasks. Data are collected automatically, using permanent, online metering devices, or using hand-held or mobile equipment operated by in-house or external technicians. Data are then dumped into a predictive maintenance software package for interpretation. Trends are plotted by the software, showing the extent and type of deterioration. Expert systems can help make sense out of the complex barrage of data collected, by determining the possible causes of deterioration and suggesting a strategy for dealing with the problem.

Many vendors of computerized maintenance management systems (CMMS) can interface their software with the data collection and diagnostic components of predictive maintenance packages to generate preventive maintenance work orders. Because the software is so specialized, however, very few CMMS vendors have actually written their own predictive maintenance module. Most of the focus group participants used the CMMS for preventive or reactive maintenance rather than predictive maintenance.

Three of the more common techniques used in a predictive maintenance program are described below.

Vibration analysis

What does it mean when your steering wheel begins to vibrate while you are cruising down the highway at 55 miles per hour?

Excessive vibration is one of the more common ways to predict equipment failure. Some experienced mechanics claim that just by listening to the hum or feeling the pulse of the equipment each day, they can detect impending mechanical problems. A more sophisticated approach is to compare actual meter readings with optimal values of frequency, amplitude and phase to determine what problems are occurring.

Vibration analysis is used primarily on rotating equipment, such as motors and turbines, to determine shaft misalignment and bearing wear. Some participants mentioned other equipment where vibration analysis was useful, including compressors, blowers and pumps.

Lubrication analysis (tribology)

What would you think if a few weeks after an oil change, your car’s oil was black? Your interpretation would depend upon whether you owned a new car or an old one, what type of lubricant you use, the operating conditions of the vehicle and so on. This complexity is precisely why expert systems are used to analyze and interpret the results of various lubricant tests. These include viscosity, flash point, total acid and base numbers and the quantity of particulate in the lubricant.

Viscosity relates to the lubricant’s ability to reduce friction created by moving parts. Maintenance costs are minimized at some optimal number of oil changes, corresponding to an acceptable range of viscosity readings. Total acid number determines the lubricant’s oxidation level, whereas total base number relates to the lubricant additives. Measuring flash-point reveals the extent of lubricant fuel dilution.

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