The Truths About Oil Analysis Data Trending

Jim Fitch, Noria Corporation
Tags: oil analysis

For many years, I've heard lab analysts advise their clients to not pay much attention to specific numerical data values (compared against a benchmark or limit) but focus instead on how the data is "trending". Indeed, trending is one of the most important and effective nomographic techniques used by diagnosticians to extract meaning and significance from time-based data. However, when this method is over applied or simply used alone, a large amount of critical information may go unnoticed or be wrongly dismissed.


Figure 1. Common Monotonic Data Trends

What is Data Trending?
The best way to trend oil analysis data is to follow its movement visually using a standard trend plot (Figure 1). Trending can quickly reveal the rate-of-change over time (slope on the plot) associated with a series of monotonic data points that might reveal a reportable condition. It can sometimes be concluded that if the rate-of-change is normal and constant (linear trend slope) that the lubricant and machine conditions are equally normal and acceptable. However, abnormal or unhealthy conditions do not always produce steep trend lines.

It is true that the use of data trending (versus level limits) overcomes certain problems or complexities that have plagued the oil analysis field for years. This works best when all other variables are locked down such as:

  1. samples taken from the same test port using the same method

  2. oil and machine service life (in hours) are known

  3. makeup rates are known

  4. when machine operating conditions and environment are constant

  5. oil type and formulation remain fixed

  6. exact same laboratory test instrument and procedure are used


Figure 2. Moisture Trend Approaching a Level Limit

When this regiment is followed, then trending can correct for differences that are often outside of the control of the laboratory. For instance, when you use trend analysis and follow the six points above, the following trend-corrupting conditions would not occur.

Dangers of Relying Only on the Trend Line
Regardless of the many valuable features in using trend analysis, there are of course a few important caveats and key applications in which the approach does not apply well alone. These limitations can be corrected by coupling trending with several other data analysis strategies. Table 1 lists common data trending limitations and possible remedies.

Despite the mentioned limitations, trend analysis is an intrinsic part of data interpretation strategy in the analysis of in-service lubricants. When combined with other alarming tactics, it can recognize such things as bad samples, an oil filter going into bypass, additive depletion, a new forcing function (abnormal wear) and wrong oil in use. While computer software is extremely helpful, it is hard to improve upon the ability to detect discernable oil analysis trends by simply plotting the data graphically and using our eyes.

Table 1. Data Trending Limitations