Even the most disciplined maintenance regimes can actually be increasing the lifecycle costs of machinery. At first glance this seems counter-intuitive doesn’t it?
Yet as a managerial discipline we seem to be aware of the detrimental effects of over-maintenance. Messing with things that are working fine, without any reason to do so, is a good way to introduce human error, reduce uptime and increase the costs of maintenance. This effect of higher activity costs for reduced performance is symptomatic of the mainly time-based maintenance thinking that most of us moved away from over the last two decades.
But where have we moved to? Today there are a range of techniques, technologies and methods that are shaping asset maintenance. However, more than anything else most of us moved to predictive maintenance technologies.
The basic thinking behind predictive maintenance technologies is; if we can predict failures with undesirable consequences in time then we can plan the corrective action and avoid costly, or dangerous, incidents from occurring. So far so good! And if we can do this without needing to stop the machinery, pull it to pieces and reassemble it again, then we are killing two birds with one stone. Avoiding consequences and increasing uptime. Even better!
In a managerial discipline where most commentators are intent on evangelizing about the latest technology, it is sometimes hard to tell people that if predictive technologies are misapplied they can cost us money, sometimes more than if we had never maintained the item at all.
Predictive maintenance works by detecting early signs of physical degradation of assets. Any system left to its own devices tends to move from order to disorder, its energy tending to be transformed into lower levels of availability, until it reaches the point of complete randomness or unavailability to do work. This is the second law of thermodynamics, and is the scientific basis for maintenance.
In practical terms degradation can be better understood looking at an item such as a bearing. Bearing failures are due mainly to metal fatigue. A good example of metal fatigue is the effect of bending a paper clip over and over until it breaks. The metal within the bearing races, balls and the cage will eventually become fatigued, until finally they begin to crack.
This is when the first signs of physical degradation begin to appear, most notably in the form of vibration. Depending on the severity of the crack they may be immediately detectable by most devices on the market, but more than likely it will take some time before they are detectable.
Once they can be detected we can then start to make judgments on how long the bearing is likely to last before we experience a functional failure. The point where it no longer does what we require of it, regardless of whether it is still working or not.
So in this case we can use vibration analysis to warn us that of a functional failure is going to occur, knowledge that we can use to plan in the corrective action, replacement of the bearing, in a way that avoids or reduces the consequences of failure.
But doesn’t it just beg the question; how did the metal become fatigued in the first place? What are the failure causes that led to this situation, and could these have been avoided? After drilling down into deeper levels of causality it suddenly becomes clear that we could be treating the symptom and not the cause of failure.
Bearings are quite complex items which have a myriad of potential failure causes, some of the more common causes of early failure include:
- Misalignment between a pump and a motor, or imbalance of the rotating element itself. All of these lead to vibration, uneven stresses, and additional load on certain parts of the bearing. This in turn speeds the process of fatigue.
- Axial thrust on the bearing. Pushing of the shaft sideways, rather than spinning around as per normal. Common where there are foreign objects passing through a pump for example.
- Over greasing of the bearing is a commonly quoted failure mode. The reason is because it leads to over heating the grease through reduced lubricant viscosity, weakening the races, balls and the cage, and increasing wear.
- The load being too far away from the bearing itself, such as with the impeller of a mixing tank, or in some cases where there are extremely long shafts between a motor and a pump. These were once commonplace in water and wastewater pumping stations.
- And of course, a bad bearing or poor installation of a bearing. Poor installation is a training and quality control issue, one that is easily rectified. But poor quality of bearings is something that I have noticed becoming more commonplace.
In these situations, even if we are able to predict the end of life component failure, the bearing will still fail before time; we will have an unnecessary downtime period, and will have to spend the money for a new bearing earlier than we should have. If the reason for this is chronic, something that will repeat itself, then we will just be installing a new bearing back into this short life cycle. Over greasing and poor alignment practices are good examples of this.
This goes to the heart of one of the most common problems when we are developing a failure management program, that of managing the asset not managing the failure modes. Predictive technology is being used in this case to paper over deeper issues, and without performing further analysis it could even look like a success, yet the result is actually a reducing our cost effectiveness!
It has been my experience that to create a truly effective predictive maintenance program; one that delivers minimum lifecycle costs for a given level of performance and risk, then one of the first steps is to identify all of the likely causes of failure at the correct level of detail.
The challenge for your reliability analysts and technicians is to know when they have analyzed the failure to enough detail, and to realize when they are starting to veer into paralysis by analysis. Once the reasonably likely failure causes have all been identified, then we can put in place the failure management strategies. Among these will be changes to operating procedures, quality control procedures, asset designs and configurations, and maintenance strategies, including the correct application of predictive technologies.
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