There are four main strategies or rather approaches to management maintenance, these are: REACTIVE, PREVENTATIVE, PREDICTIVE, RELIABILITY. This blog describe each type, but in reality most companies blend these together according to how important each asset is to the business and the risk of downtime of the asset.
Risk is a key factor in determining how asset maintenance should be performed. For different types of equipment a different approach can be taken. For example, a washroom dyer can be repaired once a failure occurs, but a factory compressor should involve preventative and possibly some predictive maintenance routines. Often this is determined by intuition and experience, but risk assessment tools can be used to determine priorities and where there may be gaps in the level of maintenance work being done.
As it suggests this is reacting to a problem or breakdown once it occurs. This is the simplest form of maintenance and suitable for low value, easily replaced or repaired equipment. This type of maintenance is very cheap because essential nothing is one until the failure. However there is a problem if this becomes a culture in an organisation; because once the assets start to become higher value and more critical to the operation, this proves to be more expensive due to higher value parts, longer repair times and lost output/use of the asset.
Preventing failure of equipment before it happens is the philosophy to apply to more valuable and important assets in your company. In order to maintain the equipment, regular maintenance tasks are scheduled to ensure the risk of failure is greatly reduced and problems are noticed before they develop into breakdowns. This type of work can include replacing wear parts, changing filters, inspections and testing the equipment. Preventative maintenance is a regular schedule of tasks such as once a week or once a year where the frequency is often guided by the original manufacturers, regulations or just down to experience.
Predictive maintenance takes preventative one step further. Whereas preventative may schedule a wear part to be changed at a set time interval, in reality the part may last longer, or if subject to adverse conditions or if it was poorly installed, may have a shorter life time. Rather than a fixed schedule, predictive maintenance uses measurements to predict when a failure may start to occur and therefore when to perform the maintenance task.
For example, a bearing in production machinery can be changed every 6 months on a preventative maintenance routine. However, since a bearing will heat up over time with excessive wear, temperature can be used as a parameter to predict when the bearing is about to fail. Rather than 1 task every 6 months to change the bearing, predictive may require monthly temperature measurements of the bearing. This information has to be collected and by monitoring the data any increase in temperature can be observed which will trigger the time to change the bearing before it fails.
Naturally this is a more intensive type of maintenance strategy. When the asset is critical and spare parts or downtime is very expensive, this approach can achieve higher levels of plant availability and reliability.
Predictive maintenance can also involve connecting sensors to production equipment and using technologies as Internet of Things (IoT) to make data collection and analysis more automated.
Reliability centred maintenance is the most complex approach to maintenance. In predictive maintenance there are some assumptions about when a failure will occur through the use of machine sensors or measurements. Reliability centred maintenance considers that all machines are different and often not linear and there are a variety of risks. In order to perform Reliability centred maintenance the maintenance team must analyse each machine separately and consider all factors and risks. It is only possible once the team have mastered the other maintenance techniques and have a very in depth and analytical approach. This is not commonly used in most industries and most CMMS systems don’t cater to this complexity.
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