- Properly used statistical techniques are powerful tools for validating and improving the alarm limits applied during evaluation of oil samples taken periodically from operating machinery.
- Alarm limits sets may be improved through the advantage afforded by using the cumulative distribution technique for evaluating alarm limit settings in nearly all measurement populations, whether normally distributed or skewed by one or more root causes.
Properly used statistical techniques are powerful tools for validating and improving the alarm limits applied during evaluation of oil samples taken periodically from operating machinery. Limitations in the application of statistical process control (SPC) may make it advantageous to use the cumulative distribution technique described in ASTM D7720 from the American Society for Testing and Materials (www.astm.org). An actual case where a serious fault was detected, trended, and corrected reveals how effective cumulative distribution can be.
Figure 1. Joey Frank tests oil samples per ASTM D7416 at his CSI minilab at the TVA Gallatin Steam Plant.
Statistical techniques, including SPC and cumulative distribution, for evaluating alarm limits for lubricating oils in steam turbines and coal pulverizers, are defined in ASTM D7720, “Standard Guide for Statistically Evaluating Measure and Alarm Limits When Using Oil Analysis to Monitor Equipment and Oil for Fitness and Contamination.”
Data gleaned from more than 1,700 coal-pulverizer oil samples and more than 2,300 steam-turbine oil samples were collected and analyzed between 2002 until 2012 by Joey Frank and Stan Sparkman of the Tennessee Valley Authority (TVA) Gallatin Steam Plant (Figure 1). The maintenance and reliability department at Gallatin Steam Plant clearly handles lubricating oil data in a consistent and proactive manner in order to implement predictive maintenance strategies, avoid unexpected shutdowns, and extend equipment longevity.
Two primary kinds of statistical evaluations for alarm limits are described in ASTM D7720. One is for normal data, and the other is for causal data. In normally distributed populations, data plotted from low to high create a bell curve where the average value is almost the same as the median, or middle, value with similar tails on left and right. Causal data distributions are typically skewed so that the average value is much higher than the median value. Something obviously causes a portion of the measurements to increase in the latter case, making SPC unsuitable for evaluating alarm limits.
According to D7720, SPC can only be used when data is in “control,” in which case the data must be normally distributed. On the other hand, the alternate statistical technique, cumulative distribution, can be used with causal data, which is skewed, typically from moderate to extremely high values. Actually, much of the data produced through machinery monitoring are causal. For example, when measuring the amount of water or iron particles in oil, the intent is to identify and correct root causes, not control. The cumulative distribution technique is well suited to such cases.
To best demonstrate the principle of cumulative distribution, data from more than 1,500 separate measurements have been employed (Tables 1 and 2). However, modest amounts of data can be used just as effectively.
ASTM D7720 states the following with respect to data population size:
- 126.96.36.199 For SPC techniques using a normal distribution, caution should be used for data sets with fewer than 30 members. Tentative limits can be set from as little as 10 samples although the quality of the limits will improve with larger populations. Larger populations (for example, in the hundreds) can provide best alarm limits. However, the data needs to be representative of the equipment population.
- 188.8.131.52 For cumulative distribution techniques regardless of the form of distribution, caution should be used for data sets with fewer than 100 members. Tentative limits can be set from as little as 50 samples although the quality of the limits will improve with larger populations. Larger populations (for example, 1000 plus) can provide best alarm limits. However the data needs to be representative of the equipment population.
|% Dielectric Change||2,319||0.8||0.4||Causal|
|Viscosity @ 40 °C||2,304||31.5||31.8||Normal|
|Table 1. Turbine Oil Data|