The paper concerns the problem of treatment of the systematic effect as a part of the coverage interval associated with the measurement result. In this case the known systematic effect is not corrected for but instead is treated as an uncertainty component. This effect is characterized by two components: systematic and random. The systematic component is estimated by the bias and the random component is estimated by the uncertainty associated with the bias. Taking into consideration these two components, a random variable can be created with zero expectation and standard deviation calculated by randomizing the systematic effect. The method of randomization of the systematic effect is based on a flatten-Gaussian distribution. The standard uncertainty, being the basic parameter of the systematic effect, may be calculated with a simple mathematical formula. The presented evaluation of uncertainty is more rational than those with the use of other methods. It is useful in practical metrological applications.
The issues connected with the complex design of various facilities, including up-to-date boiler equipment as well as the ways of organizing the space around them, are the reasons why there is often a lack of room for mounting a flowmeter in accordance with the recommendations of manufacturers. In most cases the problem is associated with ensuring sufficient lengths of straight pipe leading into and out of a flowmeter. When this condition cannot be fulfilled, the uncertainty of measurement increases above the value guaranteed by the manufacturer of the flowmeter. This sort of operation problem has encouraged the authors of this paper to undertake research aimed at the analysis of applicability of averaging Pitot tubes in the areas of flow disturbance.
The aim of this study was to estimate the measurement uncertainty for a material produced by additive manufacturing. The material investigated was FullCure 720 photocured resin, which was applied to fabricate tensile specimens with a Connex 350 3D printer based on PolyJet technology. The tensile strength of the specimens established through static tensile testing was used to determine the measurement uncertainty. There is a need for extensive research into the performance of model materials obtained via 3D printing as they have not been studied sufficiently like metal alloys or plastics, the most common structural materials. In this analysis, the measurement uncertainty was estimated using a larger number of samples than usual, i.e., thirty instead of typical ten. The results can be very useful to engineers who design models and finished products using this material. The investigations also show how wide the scatter of results is.
Measurement of the perfusion coefficient and thermal parameters of skin tissue using dynamic thermography is presented in this paper. A novel approach based on cold provocation and thermal modelling of skin tissue is presented. The measurement was performed on a person’s forearm using a special cooling device equipped with the Peltier module. The proposed method first cools the skin, and then measures the changes of its temperature matching the measurement results with a heat transfer model to estimate the skin perfusion and other thermal parameters. In order to assess correctness of the proposed approach, the uncertainty analysis was performed.
The paper deals with the accuracy of measurements of strains (elongation and necking) and stresses (tensile strength) in static room-temperature tensile strength tests. We present methods for calculating measurement errors and uncertainties, and discuss the determination of the limiting errors of the quantities measured for circular and rectangular specimens, which is illustrated with examples.
When an artificial neural network is used to determine the value of a physical quantity its result is usually presented without an uncertainty. This is due to the difficulty in determining the uncertainties related to the neural model. However, the result of a measurement can be considered valid only with its respective measurement uncertainty. Therefore, this article proposes a method of obtaining reliable results by measuring systems that use artificial neural networks. For this, it considers the Monte Carlo Method (MCM) for propagation of uncertainty distributions during the training and use of the artificial neural networks.
The objective of the paper is to analyse traceability issues in real-life gas flow measurements in complex distribution systems. The initial aim is to provide complete and traceable measurement results and calibration certificates of gas-flow meters, which correspond to specific installation conditions. Extensive work has been done to enable a more credible decision on how to deal in particular situations with the measurement uncertainty which is always subject of a flow meter’s calibration as a quantitative parameter value obtained in laboratory, and with the qualitative statement about the error of an outdoor meter. The laboratory simulation of a complex, real-life distributed system has been designed to achieve the initial aim. As an extension of standardized procedures that refer to the laboratory conditions, the proposed methods introduce additional “installation-specific” error sources. These sources could be either corrected (if identified) or considered as an additional “installation-specific” uncertainty contribution otherwise. The analysis and the results of the experimental work will contribute to more precise and accurate measurement results, thus assuring proper measurements with a known/estimated uncertainty for a specific gas flow installation. Also, the analysis will improve the existing normative documents by here presented findings, as well as fair trade in one of the most important and growing energy consumption areas regarding the legal metrology aspects. These facts will enable comparing the entire quantity of gas at the input of a complex distributed system with the cumulative sum of all individual gas meters in a specific installation.
The article presents methodology for testing the electric strength of vacuum chambers designed for modern medium voltage switchgear developed by the authors, using two innovative test stands designed and constructed by the research team above. Verification of the correctness of operation of the test stands, as well as the validity of the developed methodology was carried out by performing a series of tests. It was determined that below certain pressure values in the tested chamber (from about 5.0×10 0 Pa for station 1 and for about 4.0×10 -1 Pa for station 2), the electric strength maintains a constant value, which guarantees stable operation of the vacuum chamber. The values of the total measurement uncertainty for the electric strength tests were also estimated.
It is now widely recognized that the evaluation of the uncertainty associated with a result is an essential part of any quantitative analysis. One way to use the estimation of measurement uncertainty as a metrological critical evaluation tool is the identification of sources of uncertainty on the analytical result, knowing the weak steps, in order to improve the method, when it is necessary. In this work, this methodology is applied to fuel analyses and the results show that the relevant sources of uncertainty are: beyond the repeatability, the resolution of the volumetric glassware and the blank in the analytical curve that are little studied.
Prior knowledge of the autocorrelation function (ACF) enables an application of analytical formalism for the unbiased estimators of variance s2a and variance of the mean s2a(xmacr;). Both can be expressed with the use of so-called effective number of observations neff. We show how to adopt this formalism if only an estimate {rk} of the ACF derived from a sample is available. A novel method is introduced based on truncation of the {rk} function at the point of its first transit through zero (FTZ). It can be applied to non-negative ACFs with a correlation range smaller than the sample size. Contrary to the other methods described in literature, the FTZ method assures the finite range 1 < neff ≤ n for any data. The effect of replacement of the standard estimator of the ACF by three alternative estimators is also investigated. Monte Carlo simulations, concerning the bias and dispersion of resulting estimators sa and sa(×), suggest that the presented formalism can be effectively used to determine a measurement uncertainty. The described method is illustrated with the exemplary analysis of autocorrelated variations of the intensity of an X-ray beam diffracted from a powder sample, known as the particle statistics effect.
The electrical power drawn by an induction motor is distorted in case of appearance of a certain type of failures. Under spectral analysis of the instantaneous power one obtains the components which are connected with definite types of damage. An analysis of the amplitudes and frequencies of the components allows to recognize the type of fault.
The paper presents a metrological analysis of the measurement system used for diagnosis of induction motor bearings, based on the analysis of the instantaneous power. This system was implemented as a set of devices with dedicated software installed on a PC. A number of measurements for uncertainty estimation was carried out. The results of the measurements are presented in the paper. The results of the aforementioned analysis helped to determine the measurement uncertainty which can be expected during bearing diagnostic measurements, by the method relying on measurement and analysis of the instantaneous power of an induction machine.