Nine european national metrology institutes (NMIs) are collaborating in a new project funded by the european metrology research programme (EMRP) to establish traceable dynamic measurement of the mechanical quantities force, pressure, and torque. The aim of this joint research project (JRP) is to develop appropriate calibration methods, mathematical models, and uncertainty evaluation. The duration of the project is 3 years for a global amount of €3.6 million. It began in September 2011.

This paper is devoted to the study and implementation of real-time techniques for the estimation of time-varying, contingently correlated quantities, and relevant uncertainty. An estimation algorithm based on a metrological customization of the Kalman filtering technique is presented, starting from a Bayesian approach. Moreover, a fuzzy-logic routine for real-time treatment of possible outliers is incorporated in the overall software procedure. The system applicability is demonstrated by results of simulations performed on dimensional measurement models.

This paper describes an approach to generating reference data sets to evaluate the performance of algorithms used in coordinate metrology for form and geometric tolerance assessment. The approach starts with the reference results, e.g., the solution feature parameter values, and then determines the coordinate data. In this way, the expensive development of reference software is avoided. In this paper we consider the generation of reference data associated with determining the best-fit surface according to the least squares (Gaussian) and Chebyshev criteria.

The Statistical Feed-Forward Control Model (SFFCM) relies on a sequence of specification adjustments made on subsets of a population to counter the influence of the long time component of the variation. The difficulty strives in finding a proper estimate for the measure of the central tendency of each subset to minimize the number of the required adjustments. By means of simulating the assembly of two components having high dimensional variation, forty experiments were designed to compare the individual influence of different factors such as the number of adjustments, the sampling strategy and two measures of central tendency: the sample mean and the cumulative de-noised average. Simulation results showed that, regardless of the sampling strategy but keeping the inspection rate at 20%, the use of the cumulative de-noised average instead of the sample mean made possible to reduce the number of adjustments by 20%. Thus, while the shift mean of the resulting assembly was decreased by 90%; the standard deviation was reduced by 15%. Hence, the selection of a proper central tendency measure is crucial when modeling the long time variation. The cumulative de-noised average proved to be a valid alternative.

The Guide to the expression of uncertainty in measurement (GUM) requires, “that the results of a measurement have been corrected for all recognised significant systematic effects and that every effort has been made to identify such effects”, before the issue of evaluating their uncertainty is tackled. In addition, the GUM also requires that a model is used, expressing the functional dependence of the measurand on identified input quantities. The paper discusses the inter-relations between the so-defined two sets of influence quantities and proposes to unify their treatment.

The phase error in a high quality accelerometer was investigated on the basis of systematic experiments on a prototype platform permitting controlled vertical harmonic oscillations. The recordings of an accelerometer and of an ultra-high frequency GPS fixed on this platform were compared with those of a LVDT transducer, controlling the platform movement. All sensors were supported by independent GPS timing. The output of this study is that the recordings of accelerometers are characterized by a random phase error, which, however, decreases with the increase of the oscillation frequency, as dictated from the laws of mechanics. Such errors are important given the increasing use of combinations of accelerometers with various sensors for a wide range of applications. A simple method for the correction of such errors is proposed.

Typically, a measurement is regarded as being incomplete without a statement of uncertainty being provided with the result. Usually, the corresponding interval of measurement uncertainty will be an evaluated confidence interval, assuming that the classical, frequentist, approach to statistics is adopted. However, there are other types of interval that are potentially relevant, and which might wrongly be called a confidence interval. This paper describes different types of statistical interval and relates these intervals to the task of obtaining a figure of measurement uncertainty. Definitions and examples are given of probability intervals, confidence intervals, prediction intervals and tolerance intervals, all of which feature in classical statistical inference. A description is also given of credible intervals, which arise in Bayesian statistics, and of fiducial intervals. There is also a discussion of the term “coverage interval” that appears in the International Vocabulary of Metrology and in the supplements to the Guide to the Expression of Uncertainty in Measurement.

We consider the comparative calibration problem in the case when linear relationship is assumed between two considered measuring devices with possibly different units and precisions. The first method for obtaining the approximate confidence region for unknown parameters of the calibration line applies the maximum likelihood estimators of the unknown parameters. The second method is based on estimation of the calibration line via replicated errors-in-variables model. Essential point in this approach is approximation of the small sample distribution of the Wald-type test statistic. This enables to construct the interval estimators for the multiple-use calibration case.