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IMPROVE COST EFFECTIVENESS OF OIL SAMPLING

In our last blog (Top 5 Condition Monitoring Questions Answered) we identified some questions you can ask yourself to determine whether your condition monitoring program is delivering value to your business.


In this blog, we look at identifying some potential strategies to improve the cost-effectiveness of oil sampling, which is a standard activity in the condition monitoring programs of many businesses deploying heavy industrial equipment.


Oil sampling programs have expanded significantly over time such that up to 10 different samples can be obtained from just one rear dump truck, e.g.

  • Engine Oil
  • Transmission
  • Final Drives (2)
  • Front Hubs (2)
  • Hydraulics / Brakes
  • Steering
  • Differential
  • Coolant


If we made an assumption that an average rear dump truck operates for approximately 5,500 hours per annum, and (conservatively) has samples taken at regular 500-hour intervals, a typical truck would have 110 oil samples taken from it each year. 

Let’s assume that typical small to medium sized mining operation would have around 50 trucks in its fleet. This means the maintenance department is taking approximately 5,500 samples per annum.


If each sample kit and associated lab analysis and interpretation costs $35, the yearly off-site costs for processing these samples would be ~$192k.


But this is only half the story. The costs of obtaining the samples (drawing and labeling them) than reviewing and actioning the results must be taken into account. If we assume that each sample is taken, labeled and the results reviewed and actioned in a total of 15 minutes, each year on-site resources would spend almost 1,400 man-hours on the sample program. Almost one dedicated resource.


Finally, if it is remembered that these costs have been generated using low-frequency sampling intervals, take into account the mobile truck fleet only (i.e. do not take into account oil samples for fixed plant (e.g. conveyor gearboxes) and utilise lower cost laboratory analyses only (i.e.no filtergrams etc), it is easy to imagine a small to medium size mine spending in excess of $500k per annum on oil sampling across the entire mine site. This is an expensive exercise when considering that many sites do not get the full benefit from the information/data produced because they don’t have the time to properly assess and action it. Often we see this data identifies a clear and disturbing trend that is not actioned until it is too late, meaning that the effort invested in condition monitoring is largely wasted.


So how do we improve the cost-effectiveness of our condition monitoring programs while ensuring we reap the benefits of the information it produces?


At Relialytics we suggest a combination of practical knowledge and smart analytics can be used to optimise the amount of sampling and data review we need to undertake in our condition monitoring programs.


For example:

  • If oil sampling is necessary, why do we have to sample at fixed intervals across the life of a component? Early in the life of the component, can we use a combination of low-frequency sampling intervals in combination with the visual assessment of say a magnetic plug? As the component becomes older, can we increase the frequency of sampling? Analysis of historical data can tell us if this is a potential solution for particular components.
  • Can we use data analytics to reduce the amount of condition monitoring data that has to be reviewed by an on-site resource? The answer to this is yes.


Over the coming weeks, we will provide some practical examples of where Relialytic’s analytical techniques can be used to optimise the cost of your condition monitoring programs and improve its overall performance and benefit to your equipment. Feel free to contact us directly for more specific information.