Oil Target Cleanliness Calculator

The process of setting oil cleanliness targets is a visible, measurable performance standard. It’s like controlling your weight, blood pressure or cholesterol level by frequent measurement. These are controllable root causes of disease for those who aspire to a long, healthy life. In machine reliability, it’s proactively taking control of your machines, so they won’t take control of you.

This calculator is designed to help you create a good beginning target for both particle contamination and water contamination levels in your fluids.

Instructions
After answering all six of the questions the Target Cleanliness Level and Target Dryness will appear below the questions. The Target Cleanliness Level is displayed as an ISO Cleanliness Code. The Target Dryness is the amount of water in the oil measured in parts per million.

5 Factors to Consider When Setting Oil Cleanliness Targets

Every plant should have the goal of achieving reliability at the lowest possible cost. The reliability we seek is “optimized” reliability. This is attained through human intervention. You could say that reliability must be enabled. If left alone, machines evolve to an increasingly greater state of disrepair.

It’s like the second law of thermodynamics - things naturally move from a state of order to disorder. Let’s look at some common examples:

• Rocks wither and crumble.
• Iron rusts.
• Anything that can go wrong will go wrong (Murphy’s law).
• People grow old (as do machines).
• Modern maintenance practices age and become outdated.
• Cleanliness evolves to dirtiness.

Without human intervention, the progression to machine failure advances undeterred, but don’t be discouraged. A machine doesn’t have to end its life as a bucket of bolts. Lubrication is a reliability enabler. Contamination control is an essential enabler for lubrication excellence.

Cleanliness is one of the silent assumptions of bearing reliability (and most other machine components, too). For the others, see the sidebar below or visit https://www.machinerylubrication.com/Read/495/bearing-reliability.

Dirty lubricants should be viewed in the higher context of opportunity. It’s the lucky break that many in the reliability profession have waiting for them, i.e., the low-hanging fruit waiting to be harvested.

Defining Cleanliness

Cleanliness is almost a state of mind. One person’s cleanliness can be another person’s impending disaster. If you see no evil and hear no evil, is it still possible to have evil? If you see no dirt and feel no dirt, are your lubricants and machines to be considered clean?

The cleanliness we want is purposeful. It’s not for the sake of godliness but rather for a heightened state of reliability. Achieving cleanliness is almost always costly, yet the benefits gained are usually multiples of this cost. Just like we seek the optimum state of reliability, we should also seek the optimum state of cleanliness as a subset of reliability. Some machines require filters, but others do not. Some machines need 40-micron filters, while others aren’t optimized with anything less than 1-micron filtration.

Figure 1. Background contamination can mask the ability to enable early detection of abnormal wear conditions. High background particles in Case A result in a short detection time window compared to Case B.

For most filtered machines, contamination levels evolve to a stable state. They rise or fall on their own until stability is reached. This assumes a constant ingression rate, a constant filtration capture efficiency and a constant oil flow rate through the filter(s). Should any of these conditions change, equilibrium is lost until it is re-established later at another level. It’s a mass balance, i.e., particles entering from ingression must equal the particles removed from filtration.

This stable state of cleanliness must be within the target cleanliness level set by the reliability team. Target cleanliness should be aligned to the machine’s Optimum Reference State (ORS). For more information on the ORS, see https://www.machinerylubrication.com/Read/28659/optimum-reference-state-.

For most all machines, the correct ORS cleanliness target is driven by five important factors: criticality, environment severity, contaminant tolerance, proactive maintenance and predictive maintenance.

1. Criticality

This is a combination of the cost of repair and the cost of failure (downtime, safety, machine readiness, etc.). It is the cumulative consequences of machine failure. For more information on criticality, visit https://www.machinerylubrication.com/Read/29346/machinery-criticality-analysis.

2. Environment Severity

This relates to the likelihood of contaminant invasion and the subsequent damage to critical machine surfaces. The three main considerations are the density of contaminants in the work environment, the effectiveness of the machine to prevent ingress of these contaminants, and the ability of the filter(s) to rapidly remove and retain ingressed contaminants.

3. Contaminant Tolerance

Not all machines have the same sensitivity to particle contamination. Some are reasonably tolerant, but most are not. At least 10 percent of all critical machines have a hypersensitivity to particles of a certain size and concentration.

4. Proactive Maintenance

Proactive maintenance seeks machine life extension by systematic eradication of root causes like particle contamination. The cleaner the oil, the longer a machine’s life expectancy. Proactive maintenance brings critical root causes such as particles into focus. Noria has published extensively on this important subject.

5. Predictive Maintenance

While proactive maintenance seeks life extension, predictive maintenance seeks to detect the onset of machine failure and predict the remaining useful life (RUL). It’s a tough job, but when done with the right tools, methods and skills, it is highly effective. With oil analysis, predictive maintenance targets wear particle detection and characterization (analytical ferrography, etc.). The effectiveness of wear particle analysis is much improved when oil is clean. This can be observed in Figure 1.