Lean Maintenance: Reviewing this modern predictive technology

Today we review Lean maintenance, one of the most modern predictive technologies. Currently, many maintenance managers are already very familiar with the term “Lean” as it relates to their field, because it is a very intuitive word.

After years of popularity, even today, when industrial maintenance managers are asked about the concept of Lean maintenance, they describe it as:

• A means of cutting costs,
• Doing more with less,
• Use of the predictive maintenance to reduce the time spent on maintenance,
• Minimising downtime and maximising efficiency,
• The move towards a more planned environment, and
• Work smarter, among other things.

In this regard, today we are going to review the use of modern 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, stress, speed, alignment, and so on fall within an acceptable bandwidth.

As machines wear out, measurements deviate beyond established control limits and preventive maintenance is required to bring them back to optimal operating conditions. Therefore, equipment failure can be predicted, so that measures can be taken to prevent production stoppages and more costly emergency repairs.

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

Most maintenance managers recognise the key benefits of implementing a predictive maintenance programme, such as reducing costly downtime and improving equipment reliability. Many have implemented some level of predictive maintenance in some form. predictive maintenance.

Predictive technologies

Predictive maintenance has two components: data collection and analysis. In terms of predictive technologies, many options are available to perform these tasks. Data is collected automatically using online measuring devices or using mobile equipment operated at the plant or by external technical personnel.

The data is stored in software. predictive maintenance for interpretation. Trends are plotted by the software, showing the extent and type of deterioration in the machine. An expert system can help make sense of the complex data collected, determining the possible causes of the deterioration and suggesting a strategy to deal with the problem. 

The three most common techniques used in a Lean maintenance programme with predictive technologies are:

• Vibration analysis. Excessive vibration is one of the most helpful indicators for predicting equipment failure. Some experienced mechanics claim that simply by listening to the hum or feeling the pulse of the equipment, they can detect impending mechanical problems. A more sophisticated approach is to compare current metric readings with optimal frequency values, amplitude, and phase to determine what problems are occurring.

  Vibration analysis is primarily used on rotating equipment, such as motors and turbines, to determine shaft misalignment and bearing wear. Other equipment where vibration analysis is very useful includes compressors, fans, and pumps.

• Lubrication analysis (tribology). What happens if, a few weeks after an oil change, the oil in a car is black? Your interpretation may depend on whether it is a new or old car, what type of lubricant it uses, the operating conditions of the vehicle, and other factors. This complexity is precisely why expert systems are used to analyse and interpret the results of various lubricant tests. These include viscosity, flash point, total acid and base numbers, and the amount of particles in the lubricant.

  Viscosity refers to the ability of lubricants to reduce friction created by two or more moving parts. Maintenance costs are minimised at an optimum number of oil changes, corresponding to an acceptable range of viscosity readings. The total acid number determines the level of oxidation of the lubricant, while the total base number refers to the additives in the lubricant. Measuring the flash point reveals the degree of lubricant-fuel dilution.

  The number of particles in the lubricant is probably the most important measurement, but only a few tests have been developed to determine this. The most sophisticated methods use vision systems to collect data and computers to compare photographic images with “acceptable” images. Deviations from standard images are plotted for trend analysis and interpretation. Another method employed is a thin metal film placed in the lubricant flow. As wear particles bombard the film, it erodes, increasing its electrical resistance. Therefore, the resistance is directly proportional to the number of particles and, consequently, to the wear on the equipment.

• Infrared analysis (thermography). When a car's temperature gauge rises into the danger zone, what should be done? All equipment has a normal operating temperature range. Exceeding this range suggests that corrective action should be taken. There are more sophisticated ways to monitor equipment temperature than the thermocouples used in cars. Infrared cameras can take a “heat image” of a piece of equipment, showing different colour bands corresponding to different temperatures. Any abnormal heat patterns, trends or quantitative temperature values (“hot spots”) should be analysed and interpreted.

Common problems detected by this technique include excessive friction in rotating equipment, leaks in steam traps, damaged furnaces or boilers, and electrical overload situations. 

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