Used Oil Analysis: How to decide what is normal
by David E. Newton
Used oil analyses (UOAs) are tools. And like most tools, they can either be properly used or misused, depending upon the application, the user, the surrounding conditions, etc.=
There are already many good articles and publications in existence that tell us how to interpret the information we see in a UOA report; they speak to what elements and physical properties are indicative of certain components and conditions. It is not the intent of this article to discuss or contradict that type of information. Rather, it is the intent of this information to supplement those other articles. Most of those articles fail to address one very important topic: statistical normalcy. What is “normal” in a data set represents the typical average values and expected variation within that group. In short, it’s a matter of how to view a series of UOAs and see how results can shape our view of a healthy or ailing piece of equipment and the viability of continued lube service.
Without going deep into statistical analysis theory and education, I’ll just present what is important and helpful in understanding the data we get from UOA resources, so that reasonable decisions can be made and erroneous conclusions can be avoided. Many people have heard of the “Six-Sigma” approach using statistics, and other similar concepts. These are applicable to the world of lubricants as much as any other topic. I’ll apply these concepts to the interpretation of several series of UOAs, using real world examples to illustrate.
First, understand that statistical analysis can be applied in both small and large view-point formats. Typically these are referred to as micro-analysis and macro-analysis. I’ll differentiate the two concepts, with specific intent to address how these tools are useful in interpreting UOAs. In either case, and with rare exception, protocol dictates that one needs 30 or more samples of data to establish reasonably reliable results; it can be done with slightly less, but the data is not nearly as reliable and mathematical problems arise. Further, you cannot meld one methodology into the other for the sake of accumulating enough data; the quantities must be self-supporting. You certainly might have one or more sub-sets of full micro-data in large macro-data populations, but you should not blend the two to achieve a minimum set. In short, you cannot accumulate enough data simply by adding it from differing methodologies or duplicating it, to satisfy the minimum set requirement.
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