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Dr. Dennis Jackson
NSWC Corona-MS Interval DJ June 2002
CALIBRATION INTERVAL ANALYSIS: CURRENT AND FUTURE
Dr. Dennis Jackson
Test Equipment Reliabilityvs. Calibration Interval
Measurement Reliability (%)
0 6 12 18 24 30 36 42 48
Calibration Interval (Months)
72% EOP Reliability for GPTE
85% EOP Reliability for Safety-of-Flight and Mission Critical
(FY 2002 through April 2002)
(Based on changes made in FY 2002 Through April 2002)
Assumptions: You know when the failure occurs.
R = 1.0 at time 0.
Data: Failure Times.
R = exp(-t)
Note: The points on this graph are observed in tolerance proportions.
Assumptions: The failure occurs during an interval.
R < 1.0 at time 0.
Data: Success/Failure (Binomial)
Intercept Exponential Model
R = Ro exp(-t)
= exp(0+ 1t)
Benefits of Binomial Calibration Interval Estimation Methods
The use of Binomial estimation methods provides more accurate calibration interval estimates based on current statistical estimation theory.
Binomial estimation methods allow for alternative measurement reliability models, including intercept and multivariable models.
Better graphical tools provide more understanding of test equipment behavior.
E(d) = B0 + B1 t (Weighted Linear Regression on d)
E(res2) = C0 + C1 t (Linear Regression on res2)
Generally, a single serial number does not show increasing variability
However, several serial numbers could have slightly different slopes and intercepts:
The overall effect is one of increasing variability for the population
Benefits of UsingVariables Data
MEASURE data is often suspect
In-Tolerance data is difficult to verify (success/failure)
Engineering review required for nearly all calibration interval determinations
Variables data is more trustworthy
This could significantly increase the number of interval analyses
Variables data provides much more information
Requires fewer calibrations to accurately determine a calibration interval than In-Tolerance data
Development of automated/electronic data recording could reduce calibration time.
Calibration intervals minimize the amount of calibration effort required to keep test equipment adequately in tolerance.
Recent adjustments to calibration intervals will result in significant cost avoidance.
Near-term improvements using Binomial methods will provide better visual analysis and more accurate estimation techniques.
Long-term improvements using variables data methods will:
Fix data problems
Provide faster analyses with less data
Possibly reduce administrative part of calibration time