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The use of limits is a powerful tool in data interpretation, as well as oil analysis and different types of number-crunching activities. Limits are used to sieve key points and highlight information that might not be readily obvious, even after useful data has been selected.
The initial selection of the right limits is crucial to the long-term usefulness of limits and efficient management of data. This article looks at frequently used limits and shows how to go about the selection process in a methodical and rational manner. Too often the incorrect or insufficient limits are selected for a parameter, leading to costly errors due to misleading information.
Limits, sometimes referred to as alarms, are devices created to assist in interpreting oil analysis reports. Even if they are not formally defined, they are still used as a type of mental “limit” review process in examining the numbers. Some type of mental process is always used in examining the numbers, albeit informally.
While various parameters should exceed a certain amount, others are expected to change with time, but their rate of change remains important. Performing a mental evaluation of limits can be useful; however, when it goes beyond basic analysis, a formal system becomes necessary.
Figure 1. Limits Applied to Viscosity
Figure 1 illustrates an example of limits applied to viscosity. Sometimes limits are set above the parameter monitored (high alarms), sometimes below (low alarms), and at times, both above and below. Viscosity is an example of a parameter that is monitored with both high and low limits.
Typically, there will be both a cautionary (or alarm) limit and a critical limit. How these limits are used to take action will be discussed later.
Certain concepts must be explored before the correct type of limit can be selected. Essentially, these are the time-dependent and/or time-independent properties of the parameter(s) being limited.
For the purposes of this discussion, it is important to understand the concepts of time-dependence and time–independence.
For example, consider the acid number (AN). AN could reasonably be seen as a function of time: after a certain amount of time in use the lubricant’s AN will increase and the lubricant must be changed, thus AN would appear to be time-dependent.
Usually, AN is plotted against time, further compounding this notion. This is not true, because AN is a function of operating temperature, aeration levels, moisture content and wear particle contamination, among others.
When one considers the high AN on the lab report, the period for which the oil has been in use is actually irrelevant. How long the oil has been in use is immaterial because if it has exceeded the absolute limit, however defined, then the oil must be changed. Therefore, AN can be said to possess the property of time- independence.
As a further example consider viscosity. It is a well-established fact that viscosity will eventually increase (for most fluids), signaling a time-out on the oil. Time-dependent? Actually, no. If the viscosity has exceeded the limit, regardless of the working time for the lubricant, it is time to drain and refill. So viscosity also possesses time-independent properties.
Examine another reading on the report. The iron (Fe) reading from the elemental analysis is reported at 100 parts per million (ppm). Is that bad or good? This question can be answered only by looking at how long the oil has been in use. If it’s 100 ppm after 1,000 hours, that could be considered normal. If it’s 100 ppm after only 10 hours, there’s a problem. Therefore, the elemental analysis possesses time dependence.
To differentiate between time-dependence or time-independence, consider these questions:
Can the parameter in question be limited by an “absolute” value?
Does the parameter in question make sense if the period for which the oil is used is not considered?
If the questions above can be answered in the affirmative, then the parameter in question may be time-independent. Therefore, limits that have time-independent properties should be used. Time independence has a quality of absoluteness about it.
If, on the other hand, these questions are answered in the negative then one must apply limits with time-dependent properties. The iron reading quoted above should not be limited with an absolute value, as the reading can be expected to increase with time. Iron readings, as measured by atomic emission spectroscopy (AES), are fairly independent of filtration.
The size of the particles that can be detected by AES is very small, mostly below three microns, so filtration, unless it is very fine, tends to have a reduced effect on elemental analysis readings. The iron reading and all other wear parameters can be considered only in terms of the period for which oil is in use.
For the purposes of this discussion, a time-independent parameter is one whose actionability is not based on the length of time for which the oil has been in use. A time-dependent parameter is one whose actionability is based on the amount of time for which the oil has been in use.
Unfortunately, it gets more complicated. It was determined that AN has time- independent properties. After further examination of the lab report, an observation is made that the oil has been in use for only 100 hours, indicating a problem must have occurred to make the AN dramatically shift so soon.
Therefore, AN now appears to have a property of time-dependence. This particular parameter of an oil displays both time-dependence (in terms of its absoluteness), and time-independence in terms of the rate-of-change interest.
AN is not alone here – many properties of an oil exhibit both time independence and time dependence. Being able to discern for each property of the oil examined, it is important to consider its relationship with time in deciding what limits to use. Typically, all parameters of an oil analysis report exhibit time-dependence, however a few also possess time-independent properties.
Now that determining time-dependence and time-independence of the various different properties of an oil is possible, the next step is to examine the applicable limits.
There are two main types of limits commonly used: time-independent limits and time-dependent limits.
There are two types of time-independent limits: proactive time-independent (PTI) limits and reactive time-independent (RTI) limits. In literature, these are commonly referred to as target limits and aging limits. The term “aging” is unfortunate in that it strongly suggests a time-dependent nature, when actually there is nothing time-dependent about it.
It is useful to discuss PTI and RTI limits together because they have the same common denominator – their time-independent nature. In other words, they do not ask for an oil use timeline to determine if a given situation is normal or not. They declare “yes” or “no” as to whether a predefined absolute situation has occurred.
The main difference between PTI and RTI limits is the ability to reverse the parameters they monitor. PTI (proactive) limits are typically applied to particle counting and moisture contamination, properties of a lubricant in service which can immediately be corrected if an out-of-limit situation is found.
The factors RTI (reactive) limits monitor are not generally reversible, such as oxidation, AN increase or viscosity change.
PTI limits, as the name suggests, are proactive in nature in that once an overlimit situation is detected, a maintenance intervention can be initiated. RTI limits, on the other hand, reflect properties that are difficult or impossible to control.
If the viscosity of an oil is too high according to the limit, it must be changed. Admittedly, the properties monitored by RTI limits can sometimes be indirectly controlled, such as better temperature management to reduce AN increase, but only in a forward-moving manner. The properties monitored by an RTI limit cannot be directly controlled or reversed.
There is a gray area between PTI and RTI limits. Eventually, a moisture contamination problem can become irreversible if not corrected in time, so the erstwhile PTI limit has now become an RTI limit.
Figure 2. RTI Limit Applied to Additive Depletion on Two Different Oils
For example, consider Figure 2. The RTI limit indicates when the additive has reached a certain point. It does not indicate how fast it got there. Figure 2 simultaneously plots the additive-depletion curves for two different oils, both doing the same job, to demonstrate that knowing rate of depletion information is important.
In examining the two oils together, one can detect a difference in depletion rates. But in practice, it is more likely to have a separate plot each of Oil A and Oil B; therefore, the difference is not always as obvious. Another type of limit providing information of rate of change is necessary.