CALIBRATION INTERVAL ANALYSIS: CURRENT AND FUTURE
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CALIBRATION INTERVAL ANALYSIS: CURRENT AND FUTURE

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. CALIBRATION INTERVAL ANALYSIS: CURRENT AND FUTURE. Dr. Dennis JacksonMS30A1June 2002. Overview. Current Calibration Interval MethodsInterval Analysis ResultsNew Approaches to Calibration Interval Estimation . Current Methods: What Is a Calibration?. Compare the measurement values from a UUT with the measurement values from a calibrator.Deviation = UUT Measurement
CALIBRATION INTERVAL ANALYSIS: CURRENT AND FUTURE

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2. CALIBRATION INTERVAL ANALYSIS: CURRENT AND FUTURE

3. Overview Current Calibration Interval Methods Interval Analysis Results New Approaches to Calibration Interval Estimation

4. Current Methods: What Is a Calibration? Compare the measurement values from a UUT with the measurement values from a calibrator. Deviation = UUT Measurement ? Calibrator Measurement A UUT is considered in tolerance if: Lower Tolerance < Deviation < Upper Tolerance Measurement Reliability is the probability of being in tolerance. A Calibration Interval is the amount of time between calibrations that will meet a measurement reliability target (keeps the UUT in tolerance).

5. Current Methods: Calibration Interval Determination

6. Current Methods: Stages of the Calibration Interval Process

7. Interval Analysis Results: NAVSEA Interval Changes

8. Interval Analysis Results: Annual Calibration Cost Avoidance

9. New Approaches to Calibration Interval Estimation Near Term - Binomial Calibration Interval Estimation Methods More accurate interval estimates Alternative reliability models Visual analysis methods Long Term - Variables Data Calibration Interval Estimation Methods Fixes data problems More information on measurement characteristics Less data required MEASURE 2 capability with automated data

10. Traditional Reliability Methods

11. Tolerance Testing Data

12. Using Traditional Methods On Tolerance Testing Data

13. Reliability Methods For Tolerance Testing Data

14. Current Status of Near Term Efforts 2002 MSC Paper: ?Calibration Intervals ? New Models and Techniques? Binomial Analysis, New Models, Reliability Intercepts, Initial Variables Methods Binomial Calibration Interval Analysis System

15. 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.

16. Long Term Approach: Variables Calibration Data

17. Calibration Intervals Based on Variables Data Compute a Drift Trend. Compute a Variability Trend using residuals from the drift trend. Obtain a Reliability Curve using the drift and variability trends. Determine the Calibration Interval from the reliability curve. Predict the Measurement Uncertainty using the drift and variability trends.

18. Drift Trend Analysis E(d) = B0 + B1 t (Weighted Linear Regression on d)

19. Variability Trend Analysis E(res2) = C0 + C1 t (Linear Regression on res2)

20. A Basis for Increasing Variability

21. A Basis for Increasing Variability

22. A Basis for Increasing Variability

23. Reliability Curve Analysis

24. Determining Calibration Intervals From Variables Data

25. Current Status of Long Term Efforts 2002 MSC Paper: ?Calibration Intervals ? New Models and Techniques? Binomial Analysis, New Models, Reliability Intercepts, Initial Variables Methods 2003 MSC Paper: ?Calibration Intervals and Measurement Uncertainty Based on Variables Data? NPSL, SCE Variables Analysis Excel Tool Estimates Trends, Calibration Intervals, Measurement Uncertainty MEASURE 2 Automated/Electronic data

26. Benefits of Using Variables 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.

27. Summary 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


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