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Title: Calibration Intervals And Measurement Uncertainty Based On Variables Data
Author: Dennis Jackson
Source: 2003 Measurement Science Conference
Year Published: 2003
Abstract: Generally, the data gathered during a calibration test includes only the result of the test, that is, whether the test equipment was in or out of tolerance. The actual measurement data obtained when doing calibration testing provides a wealth of information concerning the state of the test equipment. In fact, this data is the primary source of information concerning uncertainty trends over time. This paper shows methods for using this data to predict the correct calibration interval, as well as estimating measurement uncertainty. The basic idea is to use the calibration comparison data to predict drift and variability trends using regression techniques. The regression models are then used to predict measurement reliability curves and calibration intervals that meet a reliability target. The regression models also allow the estimation of symmetric and non-symmetric measurement uncertainty limits.

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