Lean maintenance and predictive technologies

July 1, 2000
Plant Services’ readers discuss the variables involved in cutting costs

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.

The quantity of particulate in the lubricant is probably the most important measure, but only a few tests have been developed to determine it. The most sophisticated method uses vision systems to collect the data, and computers to compare digitized photographic images with “acceptable” images. Deviations from the standard image are graphed for trend analysis and then interpreted.
Another method employs a thin metallic film placed in the lubricant flow. As the wear particles bombard the film, it erodes, increasing its electrical resistance. Therefore, the film’s resistance is directly proportional to the quantity of particulate, and in turn, wear on the equipment.

Most focus group participants who said they used predictive technologies mentioned lubrication analysis as one of their key tools.

Infrared analysis (thermography)

When the temperature meter in your car climbs toward the danger zone, what do you do? All equipment has a normal operating temperature range. Exceeding that suggests corrective action should be taken. There are more sophisticated ways of monitoring equipment  temperature than the thermocouples used in automobiles. Infrared cameras can take a “heat snap-shot” of the equipment, showing different colored temperature bands. Any abnormal heat patterns, trends or quantitative temperature values (“hot spots”) must be analyzed and interpreted.

Common problems detected by this technique are excessive friction on rotating equipment, leaking steam traps, damaged ovens or furnaces and electrical overload situations. Thermography was mentioned by a few of the focus group participants.

Proactive versus reactive — finding the right balance

Much of our focus group discussion centered on describing the right balance of proactive and reactive maintenance, and how to get there. Some focus group participants lamented the role of “fire-fighter,” where too many requests for maintenance services were emergencies. Although there was much debate on the subject, it appeared that a reasonable target ratio of planned to unplanned maintenance was 80:20. There was less consensus on the optimal mix of predictive and preventive maintenance under the planned maintenance category. Those who had moved from a highly reactive environment to a more planned environment noticed improvements, such as:

  • A significant reduction in total downtime.
  • A lower spare parts inventory required in stores.
  • Increased production capacity as fewer machines lay idle or in the shop.
  • Less space required for spare parts and down equipment.
  • Fewer rush orders required.
  • Fewer quick fixes and mistakes made.
  • Improved maintenance staff utilization.
  • Less overtime required to respond to emergencies.
  • Less stress with a planned shutdown.
  • Better yield and less scrap, waste, rework, etc.
  • More predictable and stable production scheduling, so that customer responsiveness is improved.

None of them, however, felt they had achieved the optimal balance of reactive, preventive and predictive maintenance for their particular environment. (Note that a fourth option is to replace the equipment altogether when it is more costly to maintain than replace.) A further complication is that predictive technologies are becoming less expensive as technology in general improves, which changes the point of optimal balance. Participants felt that the best way to determine the optimum, was on a trial-and-error basis.

A CMMS is a useful tool to build an accurate equipment history and provide comprehensive analysis capability, said the participants. With a realistic history, they felt they could balance the
cost of replacing the equipment with maintaining it, through some optimal mix of reactive, preventive and predictive maintenance. A CMMS could help determine the total cost of downtime and poor quality as part of the optimal balance calculation. Surprisingly, not many participants tracked these costs.

Additionally, the CMMS can help identify the root cause of maintenance-related failure or quality problems, so that maintenance frequency can be reduced through prevention (e.g., training of operators) or condition monitoring (e.g., vibration analysis). This is critical to the success of any lean maintenance program. 

David Berger is the Managing Director of Grant Thornton Management Consulting in Toronto, Ontario. He is a Certified Management Consultant and a registered Professional Engineer. He is founding President of the Plant Engineering & Maintenance Association of Canada, past President of the Toronto Chapter of the Canadian Society for Industrial Engineering, and a past Vice President of the Institute of Industrial Engineers. He can be reached at [email protected]

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