Oil Sampling; Is it worth the cost and effort?

Scheduled oil analysis is a science and marketing tool that offers clients a quasi-insurance policy and sells the notion that if regular and frequent ‘oil sampling’ is carried out as recommended, catastrophic failure and resultant high repair costs are preventable. Furthermore, oil change frequencies can be extended when the adverse effects of oxidation and viscosity are measured and understood. This aspect alone amounts to huge savings. So far, oil analysis sounds fair and reasonable, but what is the cost-benefit?

Without sounding too cynical, I pose these questions to the maintenance practitioners of large equipment fleets and production assets.

1. Who designed the oil testing regime/program in the organisation?

2. Who verifies the current program adequacy and maintains its relevance over time?

3. Is there a tipping point where the investment is no longer economically feasible, assuming there is one?

4. How often is the system reviewed and improved?

Table 1 below provides the basis for understanding notional annual oil analysis costs for a standard 4 test in a sizable mining fleet of 40 haul trucks with the usual complement of face shovels, loaders, graders, dozers, drills, and ancillaries.

The actual annual cost of oil sampling could exceed my earlier estimate of $103,880, based on multiple components such as drive gearboxes, transmissions, planetary drives, hoists, winches, and so on. In reality, the cost of oil sampling for a mature fleet will likely range from $60,000 to $100,000 per annum. It's important to note that this figure doesn't include the cost of management time, CMMS data processing, or additional random sampling. The dichotomy at play here is:

1. Sample data is standard, and no action or remedial cost is required.

2. Sample data exhibits incipient failure, which may carry a remedial cost of ~20% of the changeout value.

3. Significant damage has already occurred whereby changeout is 70% - 80% of new replacement value.

A sizable rebuilt 2500kW engine can cost between $400k and $700k, while catastrophic hydraulic system failure - depending on the asset - can be just as expensive. In this context, an annual insurance premium of around $100k makes sound economic sense for equipment worth tens of millions of dollars, even when calculated as a percentage of invested capital. Furthermore, based on experience, substantial fleet owners can expect to encounter 2-3 catastrophic equipment failures each year, resulting in repair expenses of $2-3 million.

In addition to direct cost losses, equipment failures can have significant safety and environmental implications. For instance, a component failure caused by contamination could cause a face shovel to dump 2000 litres of hydraulic oil on the ground, while a wheel lock-up on a stockpile loader could lead to accidents and injuries. Contaminant-related equipment failures can result in accident interventions, oil and fuel fires, physical injuries, and environmental damage. When you factor in the impact on reliability, productivity, and indirect cost losses, the financial impact on the business can be enormous - often, the cost of repairs. Moreover, such incidents can also be embarrassing for the company.

So, what does all this mean? Many, but not all, maintenance departments have tightened their oil analysis process by expediting tests, tracking results, and monitoring them closely. The results are scrutinized and entirely understood by all stakeholders, who take any necessary actions. This rule should also extend to oil storage and bulk mobile service tanks. Alternatively, modern technology can provide innovative fleet owners of heavy equipment with the option of owning an in-house laboratory for testing. This yields enormous benefits like;

1. Testing on demand, faster turnaround - saving failure delays due to slow turnaround and reporting.

2. Cloud-based database with a proprietary CMMS interface - efficient data processing and reporting.

3. Batch test timelines and schedules that suit the business - configurable.

4. Substantial control over the total process and procedures.

5. Meagre sample unit cost of less than one dollar - significant annual savings.

6. Ability to increase the number of samples at a much lower in-house cost.

7. Ability to train an employee and add testing to his/her regular job as a part-time occupation.

8. Little capital investment for the hardware. Preventing one major failure will pay back the investment.

9. Analyses take only minutes and are not overly complicated or scientific.

Personally, I prefer the in-house oil analysis model, provided the economics add up.