Machinery Lubrication

Machinery Lubrication Jan Feb 2016

Machinery Lubrication magazine published by Noria Corporation

Issue link:

Contents of this Issue


Page 30 of 89

26 | January - February 2016 | It is important to note that even when experiencing extremely consistent condi- tional and resource inputs, there is variation, even when the process is in control. You need a considerable amount of data from this single source to define what is average and normal. This takes time, money and patience. Macro-analysis does not look at just one entity but all those in a desired grouping. It predicts the behavior (results) of the mass population's reaction to changing condi- tions (multiple inputs). With this method, you can look at a large group of data that represents a piece of equipment (engine, gearbox, differential, transmission, etc.) from different points of origin and deter- mine what is "normal" across a broad base of applications. Macro-analysis comes much quicker because multiple sources are accepted. However, caution must be used to ensure that illogical conclusions are not drawn based upon false presumptions or in confusing correlation with causation. Micro-analysis of Data from a Single Engine Table 1 is a good example of micro-anal- ysis for a V-6 gasoline engine. Oil changes were performed religiously, the inputs were consistent and the owner was dedi- cated to the testing parameter protocol. The vehicle saw very typical use in its life cycle and environment, including weather, driving cycles, etc. In this example, the data created was consistent and could be used to make a sound decision for the stated operating conditions. No abnormalities were revealed. The standard deviations were all well below the means, which was as expected and desired in a controlled micro-data set. The vehicle went from a steady diet of a synthetic oil with a premium filter to a quality conventional oil with an off-the- shelf filter. The data shows that the average wear metals shifted less than a point after this change. All shifts were well within one standard deviation for each distinct metal. What can be surmised from these results is that there was no tangible benefit to using the high-end products for this main- tenance plan and operational pattern. Conversely, the typical quality baseline products presented no additional risk of accelerated wear. It cannot be concluded that this result would be true in all potential circumstances, only that it is true when applied to a 5,000-mile oil change interval with the given operating conditions. Signifi- cantly longer oil change intervals likely may have shown a statistical difference between the two lube/filter choices, but that was not part of the test protocol. Macro-analysis of Data from Numerous Engines The following examples of macro-anal- ysis illustrate how mass-market data can be used. The first set of data is from a V-8 gasoline engine. In Table 2, note the two columns for lead (Pb). One is the raw data, while the other is the same data stream with three data points removed because they were affecting the "normalcy" of the data. Most of the lead counts in all the other samples were well below 35 parts per million (ppm), but three samples had lead counts of 68 ppm, 204 ppm and 602 ppm. When the individual results were reviewed, there was no reasonable explanation as to why the lead was so high in these three reports. In Table 3, you can see how greatly those three data points were skewing the results. Notice how the average lead count dropped more than 57 percent, and the standard deviation decreased by nearly a factor of 10. Only three samples of 548 were responsible for such an overt act of skewing the data. This is where math and common sense come together to form a reasonable conclusion that some interven- tion of the data is warranted. By removing only 0.5 percent of the lead data popula- tion, the range shifted significantly. This indicates that those three samples were not "normal," and the remaining 99.5 percent were. In macro-data, when the standard devi- ation is some multiple larger than the mean, there is cause to believe abnormalities are imbedded in the data stream. When the deviation is smaller, it indicates the mass- market population is representing the variability of inputs as desired and not being affected by spoilers. Unfortunately, there is no hard and fast rule. Training, experience and knowledge of the subject matter will help define and delineate when and where to intervene. In examining the results through the years, there clearly were not any significant changes over time. For example, the average iron wear rate was reasonably consistent and varied by less than 1 part OIL MILES VEHICLE MILES ALUMINUM CHROMIUM IRON COPPER LEAD 5,002 49,997 3 1 14 4 3 5,028 104,993 3 1 11 3 3 5,065 154,941 2 3 14 5 6 5,019 204,983 5 1 13 3 4 5,019 254,836 2 3 12 2 4 4,960 284,815 3 2 13 6 4 OIL MILES VEHICLE MILES ALUMINUM CHROMIUM IRON COPPER LEAD 4,996 N/A Average 3.7 1.4 14.4 4.2 4.0 52 N/A Standard Deviation 1.3 0.6 2.1 1.7 1.5 5,151 N/A Upper Limit 7.6 3.2 20.7 9.3 8.6 5,102 284,815 Max. 6.0 3.0 18.0 8.0 8.0 PPM per 1,000 Miles 0.7 0.3 2.9 0.8 0.8 Table 1. An example of micro-analysis for a V-6 gasoline engine OIL ANALYSIS

Articles in this issue

Links on this page

Archives of this issue

view archives of Machinery Lubrication - Machinery Lubrication Jan Feb 2016