Measurement Uncertainty Analysis

Castrup, H.: Estimating Parameter Bias Uncertainty,  Presented at the Measurement Science Conference, Anaheim, CA, March 2006 (39 pgs).

Abstract: Type B, ANOVA and Bayesian methods are presented for estimating the uncertainty in the bias of measurement references and the uncertainty in parameter deviations from nominal.

Castrup, H.: Note on Type B Degrees of Freedom Equation, ISG Technical Document, September 2004. (3 pgs)

Castrup, S.: Why Spreadsheets are Inadequate for Uncertainty Analysis,  Presented at the 8th Annual ITEA Instrumentation Workshop, Lancaster, CA, May 2004 (18 pgs).

Abstract: This paper discusses key questions and concerns regarding the development of measurement uncertainty analysis worksheets or custom add-in programs for Excel or Lotus spreadsheet applications.

Castrup, H.: Estimating and Combining Uncertainties,  Presented at the 8th Annual ITEA Instrumentation Workshop, Lancaster, CA, May 2004 (7 pgs).

Abstract: This paper presents a general model for  measurement uncertainty analysis that can be applied to the analysis of direct measurements and multivariate measurements.

Castrup, S.: A Comprehensive Comparison of Uncertainty Analysis Tools, Presented at the Measurement Science Conference, Anaheim, CA, January 2004 (27 pgs). Updated June 2005.

Abstract: This paper presents a comprehensive review and comparison of several measurement uncertainty analysis software products and freeware that have been developed in the past several years.  Methodology, functionality, user- friendliness, documentation, technical support and other key criteria are addressed. Suggestions for selecting and using the various measurement uncertainty analysis tools are also provided.

Castrup, H.: Estimating Bias Uncertainty, Proceedings of the NCSLI  Workshop & Symposium,  Washington D.C., July  2001. (20 pgs)
Abstract:  Methods for estimating the uncertainty in the bias of reference parameters or artifacts are presented. The methods are cognizant of the fact that, although such biases persist from measurement to measurement within a given measurement session, they are, nevertheless, random variables that follow statistical distributions. Accordingly, the standard uncertainty due to measurement bias can be estimated by equating it with the standard deviation of the bias distribution. Since the measurement bias of a reference is a dynamic quantity, subject to change over the calibration interval, both uncertainty growth and parameter interval analysis are also discussed.

Castrup, H.: Distributions for Uncertainty Analysis, Proceedings of the International Dimensional Workshop,  Knoxville, TN, May  2001. (Updated, 12 pgs)
Abstract:  This paper describes statistical distributions that can be applied to both Type A and Type B measurement errors and to equipment parameter biases. Once the statistical distribution for a measurement error or bias is characterized, the uncertainty in this error or bias is computed as the standard deviation of the distribution. For Type A estimates, the distribution or "population" standard deviation is estimated by the sample standard deviation. For Type B estimates, the standard deviation is computed from limits, referred to as error containment limits and from probabilities, referred to as containment probabilities. The degrees of freedom for each uncertainty estimate can often be determined, regardless of whether the estimate is Type A or Type B.
 
Castrup, H.: Uncertainty Growth Estimation in UncertaintyAnalyzer, ISG Technical Document, August 2000. (9 pgs)

Castrup, H.: An Investigation into Estimating Type B Degrees of Freedom, ISG Technical Document, August 2000. (4 pgs)

Castrup, H.: Estimating Category B Degrees of Freedom, Proc. Measurement. Science Conf., Anaheim, CA, January 2000. (8 pgs)
Abstract: A method is presented for estimating uncertainties in cases where samples of data are unavailable. The method includes a formalism that provides a structure for extracting information from the measurement experience of scientific or technical personnel. This information is used to both estimate uncertainties and to approximate the degrees of freedom of the estimate.  Using these results, confidence limits are developed that obviate the need for arbitrary coverage factors and misleading expanded uncertainties.

Castrup, H.: Uncertainty Analysis and Parameter Tolerancing, Proc. NCSL Workshop & Symposium, Dallas, TX, July 1995. (16 pgs)
Abstract:  An uncertainty analysis methodology is described that is applicable to establishing and testing equipment parameter tolerances. The methodology develops descriptions of measurement uncertainty that relate directly to whether parameters will be acceptable for intended applications.  An example is presented that illustrates the concepts involved.

Castrup, H.: Practical Methods for Analysis of Uncertainty Propagation, Proc. 38th Annual ISA Instrumentation Symposium, Las Vegas, NV, April 1992. (25 pgs)
Abstract: An uncertainty analysis methodology is described that is relevant to equipment tolerancing, analysis of experimental data, development of manufacturing templates and calibration standards. By assembling the methodology from basic measurement principles, controversies regarding uncertainty combination are avoided.  

Measurement Decision Risk Analysis
Castrup, H.: Analyzing Uncertainty for Risk Management, Proc. ASQC 49th Annual Quality Congress, Cincinnati, OH, May 1995. (13 pgs)
Abstract: A structured approach to uncertainty analysis is described that is applicable to product quality assessment and risk management.  Expressions are derived that incorporate estimated uncertainties in risk analysis to determine whether product parameters will be acceptable for intended applications.

Castrup,  H.: Uncertainty Analysis for Risk Management, Proc. Measurement. Science Conf., Anaheim, CA, January 1995. (27 pgs)
Abstract: Measurement errors and error models are reviewed and measurement process error components are described. Measurement decision risks are estimated based on the results of an uncertainty analysis example and risk management considerations are outlined.  Classical measurement decision risk is also discussed, with special emphasis on the impact of process uncertainty on false accept and false reject risks. A new method for computing risks is given in Appendix B.

Statistical Process Control
Castrup, H.: Risk-Based Control Limits, Proc. Measurement. Science Conf., Anaheim, CA, January 2001. (9 pgs)
Abstract:  A methodology is presented for the development of SPC control limits for measurement processes.  The methodology employs both Bayesian and traditional measurement decision risk concepts to establish control limits that flag whether measuring processes are in or out of control relative to the specifications of the artifacts they measure. The methodology has particular relevance for calibration and testing.

Castrup, H.: Analytical Metrology SPC Methods for ATE Implementation, Proceedings of the NCSLI  Workshop & Symposium,  August 1991, Albuquerque, NM. (16 pgs)
Abstract:  Probability theory is employed to develop an analytical metrology SPC methodology which is amenable to implementation in automated testing environments.  The methodology can be used to obtain  in-tolerance probability estimates  and  bias  estimates  for  both  test  systems  and  units  under  test  without  recourse  to  external  measurement standards.  This makes it particularly applicable in remote environments where measuring instruments are expected to function without calibration for extended periods of time.

Abstract:  This paper describes a methodology for determining calibration intervals from variables data.  A regression analysis approach is developed and algorithms are given for setting parameter calibration intervals from the results of variables data analysis.