Measurement Uncertainty Analysis of the Rotary-scan Method for the Measurable Dimension of Cylindrical Workpieces

The measurement uncertainty analysis is carried out to investigate the measurable dimensions of cylindrical workpieces by the rotary-scan method in this paper.Due to the difficult alignment of the workpiece with a diameter of less than 3 mm by the rotary scan method,the measurement uncertainty of the cylindrical workpiece with a diameter of 3 mm and length of 50 mm which is measured by a roundness measuring machine,is evaluated according to GUM(Guide to the Expression of Uncertainty in Measurement)as an example.Since the uncertainty caused by the eccentricity of the measured workpiece is different with the dimension changing,the measurement uncertainty of cylindrical workpieces with other dimensions can be evaluated the same as the diameter of 3 mm but with different eccentricity.Measurement uncertainty caused by different eccentricities concerning the dimension of the measured cylindrical workpiece is set to simulate the evaluations.Compared to the target value of the measurement uncertainty of 0.1μm,the measurable dimensions of the cylindrical workpiece can be obtained.Experiments and analysis are presented to quantitatively evaluate the reliability of the rotary-scan method for the roundness measurement of cylindrical workpieces.

Measurement Uncertainty Evaluation of Conicity Error Inspected on CMM

The cone is widely used in mechanical design for rotation, centering and fixing. Whether the conicity error can be measured and evaluated accurately will directly influence its assembly accuracy and working performance. According to the new generation geometrical product specification（GPS）, the error and its measurement uncertainty should be evaluated together. The mathematical model of the minimum zone conicity error is established and an improved immune evolutionary algorithm（IlEA） is proposed to search for the conicity error. In the IIEA, initial antibodies are firstly generated by using quasi-random sequences and two kinds of affinities are calculated. Then, each antibody clone is generated and they are self-adaptively mutated so as to maintain diversity. Similar antibody is suppressed and new random antibody is generated. Because the mathematical model of conicity error is strongly nonlinear and the input quantities are not independent, it is difficult to use Guide to the expression of uncertainty in the measurement（GUM） method to evaluate measurement uncertainty. Adaptive Monte Carlo method（AMCM） is proposed to estimate measurement uncertainty in which the number of Monte Carlo trials is selected adaptively and the quality of the numerical results is directly controlled. The cone parts was machined on lathe CK6140 and measured on Miracle NC 454 Coordinate Measuring Machine（CMM）. The experiment results confirm that the proposed method not only can search for the approximate solution of the minimum zone conicity error（MZCE） rapidly and precisely, but also can evaluate measurement uncertainty and give control variables with an expected numerical tolerance. The conicity errors computed by the proposed method are 20%-40% less than those computed by NC454 CMM software and the evaluation accuracy improves significantly.

Measurement uncertainty in pharmaceutical analysis and its application

The measurement uncertainty provides complete information about an analytical result. This is very important because several decisions of compliance or non-compliance are based on analytical results in pharmaceutical industries. The aim of this work was to evaluate and discuss the estimation of uncertainty in pharmaceutical analysis. The uncertainty is a useful tool in the assessment of compliance or non-compliance of in-process and final pharmaceutical products as well as in the assessment of pharmaceutical equivalence and stability study of drug products.

Adaptive cubature Kalman filter based on variational Bayesian inference under measurement uncertainty

A novel variational Bayesian inference based on adaptive cubature Kalman filter(VBACKF)algorithm is proposed for the problem of state estimation in a target tracking system with time-varying measurement noise and random measurement losses.Firstly,the Inverse-Wishart(IW)distribution is chosen to model the covariance matrix of time-varying measurement noise in the cubature Kalman filter framework.Secondly,the Bernoulli random variable is introduced as the judgement factor of the measurement losses,and the Beta distribution is selected as the conjugate prior distribution of measurement loss probability to ensure that the posterior distribution and prior distribution have the same function form.Finally,the joint posterior probability density function of the estimated variables is approximately decoupled by the variational Bayesian inference,and the fixed-point iteration approach is used to update the estimated variables.The simulation results show that the proposed VBACKF algorithm considers the comprehensive effects of system nonlinearity,time-varying measurement noise and unknown measurement loss probability,moreover,effectively improves the accuracy of target state estimation in complex scene.

Calculation of Measurement Uncertainty for Stiffness Modulus of Asphalt Mixture

Asphalt mixture is a highly heterogeneous material, which is one of the reasons for high measurements uncertainty when subjected to tests. The results of such tests are often unreliable, which may lead to making bad professional judgments. They can be avoided by carrying out reliable analyses of measurement uncertainty adequate for the research methods used and conducted before the actual research is done. This paper presents the calculation of measurements uncertainty using as an example--the determination of the stiffness modulus of the asphalt mixture, which, in turn, was accomplished using the indirect tension method. The paper also shows the employment of the basic methods of statistical analysis, such as testing two mean values and conformity tests. Essential concepts in measurements uncertainty have been compiled and the determination of the stiffness module parameters are discussed. It has been demonstrated that the biggest source of error in the stiffness modulus measuring process is the displacement measure. The aim of the research was to find the measurement uncertainty for stiffness modulus by an indirect tensile test and the presentation of examples of the used statistical methods.

Evaluation of Dosimetric Impact of Uncertainty of Measurement in Estimating External Radiotherapy Dose

Cancer is a major societal public health and economic problem, responsible for one in every six deaths. Radiotherapy is the main technique of treatment for more than half of cancer patients. To achieve a successful outcome, the radiation dose must be delivered accurately and precisely to the tumor, within ± 5% accuracy. Smaller uncertainties are required for better treatment outcome. The objective of the study is to investigate the uncertainty of measurement of external radiotherapy beam using a standard ionization chamber under reference conditions. Clinical farmers type ionization chamber measurement was compared against the National Reference standard, by exposing it in a beam 60Co gamma source. The measurement set up was carried out according to IAEA TRS 498 protocol and uncertainty of measurement evaluated according to GUM TEDDOC-1585. Evaluation and analysis were done for the identified subjects of uncertainty contributors. The expanded uncertainty associated with 56 mGy/nC ND,W was found to be 0.9% corresponding to a confidence level of approximately 95% with a coverage factor of k = 2. The study established the impact of dosimetry uncertainty of measurement in estimating external radiotherapy dose. The investigation established that the largest contributor of uncertainty is the stability of the ionization chamber at 36%, followed by temperature at 22% and positioning of the chamber in the beam at 8%. The effect of pressure, electrometer, resolution, and reproducibility were found to be minimal to the overall uncertainty. The study indicate that there is no flawless measurement, as there are many prospective sources of variation. Measurement results have component of unreliability and should be regarded as best estimates of the true value. .

Evaluation of measurement uncertainty based on grey system theory for small samples from an unknown distribution

To evaluate measurement uncertainty for small sample size and measurement data from an unknown distribution, we propose a grey evaluation method of measurement uncertainty based on the grey relation coefficient. The uncertainty of measurement is analyzed using grey system theory, and the defects of the grey evaluation model of measurement uncertainty (GEMU) are studied. We then establish an improved grey evaluation model of measurement uncertainty (IGEMU). Simulations show that the precision of IGEMU is greater than that of GEMU, and that sample size has only a small effect on the precision of IGEVU. In particular, IGEMU is applied to evaluating measurement uncertainty for small sample size and measurement data from an unknown distribution. The measurement uncertainty of total profile deviation, which is measured by the CNC gear measuring center, can be evaluated by a combination of IGEMU and the Monte Carlo method.

Research into Uncertainty in Measurement of Seawater Chemical Oxygen Demand by Potassium Iodide-Alkaline Potassium Permanganate Determination Method

Using the glucose and L-glutamic-acid to prepare the standard substance according to the ratio of 1：1, and the artificial seawater and the standard substance to prepare a series of standard solutions, the distribution pattern of uncertainty in measurement of seawater COD is obtained based on the measured results of the series of standard solutions by the potassium iodide-alkaline potassium permanganate determination method. The distribution pattern is as follows： Uncertainty in measurement is big and not constant at the high end, but small and constant at the low end.

Comparison of GUF and Monte Carlo methods to evaluate task-specific uncertainty in laser tracker measurement

Measurement uncertainty plays an important role in laser tracking measurement analyses. In the present work, the guides to the expression of uncertainty in measurement(GUM) uncertainty framework(GUF) and its supplement, the Monte Carlo method, were used to estimate the uncertainty of task-specific laser tracker measurements. First, the sources of error in laser tracker measurement were analyzed in detail, including instruments, measuring network fusion, measurement strategies, measurement process factors(such as the operator), measurement environment, and task-specific data processing. Second, the GUM and Monte Carlo methods and their application to laser tracker measurement were presented. Finally, a case study involving the uncertainty estimation of a cylindricity measurement process using the GUF and Monte Carlo methods was illustrated. The expanded uncertainty results(at 95% confidence levels) obtained with the Monte Carlo method are 0.069 mm(least-squares criterion) and 0.062 mm(minimum zone criterion), respectively, while with the GUM uncertainty framework, none but the result of least-squares criterion can be got, which is 0.071 mm. Thus, the GUM uncertainty framework slightly underestimates the overall uncertainty by 10%. The results demonstrate that the two methods have different characteristics in task-specific uncertainty evaluations of laser tracker measurements. The results indicate that the Monte Carlo method is a practical tool for applying the principle of propagation of distributions and does not depend on the assumptions and limitations required by the law of propagation of uncertainties(GUF). These features of the Monte Carlo method reduce the risk of an unreliable measurement of uncertainty estimation, particularly in cases of complicated measurement models, without the need to evaluate partial derivatives. In addition, the impact of sampling strategy and evaluation method on the uncertainty of the measurement results can also be taken into account with Monte Carlo method, which plays a guiding role in measurement planning.

Bernoulli particle flter with observer altitude for maritime radiation source tracking in the presence of measurement uncertainty

For maritime radiation source target tracking in particular electronic counter measures（ECM）environment,there exists two main problems which can deteriorate the tracking performance of traditional approaches.The frst problem is the poor observability of the radiation source.The second one is the measurement uncertainty which includes the uncertainty of the target appearing/disappearing and the detection uncertainty（false and missed detections）.A novel approach is proposed in this paper for tracking maritime radiation source in the presence of measurement uncertainty.To solve the poor observability of maritime radiation source target,using the radiation source motion restriction,the observer altitude information is incorporated into the bearings-only tracking（BOT）method to obtain the unique target localization.Then the two uncertainties in the ECM environment are modeled by the random fnite set（RFS）theory and the Bernoulli fltering method with the observer altitude is adopted to solve the tracking problem of maritime radiation source in such context.Simulation experiments verify the validity of the proposed approach for tracking maritime radiation source,and also demonstrate the superiority of the method compared with the traditional integrated probabilistic data association（IPDA）method.The tracking performance under different conditions,particularly those involving different duration of radiation source opening and switching-off,indicates that the method to solve our problem is robust and effective.

Theoretical analysis and verification of uncertainty of measurement on a cantilever coordinate measuring machine

A cantilever coordinate measuring machine(CCMM)is proposed according to the in-situ measurement requirement of workpieces with complex structures limited by the finite space of the5-axes computerized numerical control(CNC)processing site.Factors affecting uncertainty of measurement(UM)are classified and analyzed on the basis of uncertainty evaluation criteria,and the estimation technique of UM for measuring systems is presented.UM of the CCMM is estimated from the factors such as temperature,error motions as well as the mechanism deformations.Measurement results show that the actual measurement error is smaller than that of measurement requirement.

High-precision inductance measurement system based on double-excitation auto-balancing bridge

In order to achieve high precision measurement of inductance in a wide frequency range,a method of inductance measurement based on double-excitation auto-balancing bridge is proposed.In this method,the direct digital synthesizer(DDS)as signal generator is used as the bridge excitation source,and the bridge is automatically balanced by adjusting and measuring the voltage ratio.Using standard resistors,the system can achieve high precision measurement of four-terminal pair inductors in the frequency range of 100Hz-100kHz.Aiming at the low efficiency of bridge balancing,an iterative balancing algorithm based on the steepest descent method is proposed.In order to suppress the interference caused by the initial phase change and non-integer periodic sampling,the high-precision measurement of the complex impedance of inductance is realized based on the all-phase fast Fourier transform(apFFT).Finally,the corresponding measurement system is built and the inductance measurement experiment is carried out.The experimental results show that the relative error of the system for inductance measurement can be as low as 0.009%,and the optimal relative measurement uncertainty of the system can reach 9.89×10^(-4)compared with 5×10^(-5)of commercial impedance analyzer.

A Possibilistic Approach for Uncertainty Representation and Propagation in Similarity-Based Prognostic Health Management Solutions

In this paper, a data-driven prognostic model capable to deal with different sources of uncertainty is proposed. The main novelty factor is the application of a mathematical framework, namely a Random Fuzzy Variable (RFV) approach, for the representation and propagation of the different uncertainty sources affecting Prognostic Health Management (PHM) applications: measurement, future and model uncertainty. In this way, it is possible to deal not only with measurement noise and model parameters uncertainty due to the stochastic nature of the degradation process, but also with systematic effects, such as systematic errors in the measurement process, incomplete knowledge of the degradation process, subjective belief about model parameters. Furthermore, the low analytical complexity of the employed prognostic model allows to easily propagate the measurement and parameters uncertainty into the RUL forecast, with no need of extensive Monte Carlo loops, so that low requirements in terms of computation power are needed. The model has been applied to two real application cases, showing high accuracy output, resulting in a potentially effective tool for predictive maintenance in different industrial sectors.

Evaluation of Uncertainty in the Determination of Ozone Concentration in the Air by Sodium Indigo Disulfonate Spectrophotometry

The uncertainty of the determination of ozone concentration in the air at a workplace by sodium indigo disulfonate spectrophotometry was evaluated.The results show that the expanded uncertainty of ozone concentration U was 0.016 mg/m^(3),and the main factor affecting the measurement uncertainty of ozone concentration was the uncertainty introduced during sample collection.

Limits of Level 3 Fair Value Measurement： Based on Conceptual Framework

The IASB stated that in the review of the conceptual framework, if measurement uncertainty is very high, a measurement basis different from fair value may provide more appropriate information. Level 3 fair value measurements are inappropriate for areas （called ＂Level 4＂ by the author） in which models are not formulated and that have material uncertainty in measurement. The purpose of this paper is to elucidate ＂measurement uncertainty＂, which impacts ＂faithful representation＂ in fair value measurement, from the perspective of ＂verifiability＂. To do so, it will employ a conceptual examination of the limits of Level 3 fair value measurement based on IFRS 13 （Fair Value Measurement）. While taking into consideration recent discussions on reviewing conceptual framework, the author will reorganize the conceptual categories of verifiability （i.e., clarification of the ＂reasonable verifiability＂ categories） to underpin the fact that material uncertainties in measurement have obstructed constructing faithful representation. In addition, the author will discuss the problem that the dichotomy of direct and indirect found in IASB is not accurate enough to fully understand the concept of verifiability. In this context, the author will also suggest that the concept of verifiability, reorganized through ＂reasonable verifiability＂, can satisfy the requirements for constructing faithful representation.

Rotation Measurement Using Speckle Photography with LiNbO3 Crystal: Theoretical and Experimental Analysis

Photorefractive crystals present varied features charming presence, such as high resolution, and normal handling. Depending on the portability of erasing images, photorefractive crystals are convenient for read-write implementations and hence find potential use in speckle photography, speckle interferometry, image processing and holography. A two-beam coupling arrangement using a LiNbO3 crystal as a recording medium for real-time rotation measurement using the coherent and low-power laser source is presented in this paper. A speckle photography technique is advanced for the measurement of a small rotation of a transmitted glass slide sample. New theoretical analysis is formulated for a general case of a slide rotation. Experimental studies are carried out to verify the outcome of the theoretical predictions and accuracy of measurement. Uncertainty of rotation measurement is studied and quantified. The proposed technique is a simple, attractive and alternative method for fringe analysis. The method promises a high degree of accuracy and increased range for rotation measurement in real-time.

Determination of Heavy Metals in Tobacco by ICP-MS and Analysis of Uncertainty

The research aims to set up the determination method of heavy metals in tobacco by ICP-MS and analyze its uncertainty. Microwave digest technique is used for decomposition of the tobacco samples. Bi,Sc,Ge and In are employed as internal standards to eliminate the interference of the matrix. A method is developed for simultaneous determination of Cr,Cu,As,Cd,Hg and Pb in tobacco from Yunnan Province by ICP-MS,and the measurement uncertainty is evaluated by metrological method. The test results indicate that in the content range of0- 1. 0 g / ml,the linearly dependent coefficient of six elements is 1. 0000,and the RSD achieves 1. 5%- 11%( n = 8). The elemental content in tobacco is as follows: Cr( 2. 92,0. 24) mg / kg,Cu( 3. 64,0. 24) mg / kg,As( 0. 467,0. 025) mg / kg,Cd( 3. 12,0. 15) mg / kg,Hg( 0. 035,0. 006) mg/kg,Pb( 4. 62,0. 36) mg/kg. The uncertainty of sample size,the constant volume of the sample solution can be neglected. It is concluded that the method is accurate,simple,sensitive and rapid.

Fire risk evaluation research on fully mechanized coalface based on the uncertainty measure theory

A relatively perfect coalmine fire risk-evaluating and order-arranging model that includes sixteen influential factors was established according to the statistical information of the fully mechanized coalface ground on the uncertainty measure theory. Then the single-index measure function of sixteen influential factors and the calculation method of computing the index weight ground on entropy theory were respectively established. The value assignment of sixteen influential factors was carried out by the qualitative analysis and observational data, respectively, in succession. The sequence of fire danger class of four experimental coalfaces could be obtained by the computational aids of Matlab according to the confidence level criterion. Some conclusions that the fire danger class of the No.l, No.2 and No.3 coalface belongs to high criticality can be obtained. But the fire danger class of the No.4 coalface belongs to higher criticality. The fire danger class of the No.4 coalface is more than that of the No.2 coalface. The fire danger class of the No.2 coalface is more than that of the No.1 coalface. Finally, the fire danger class of the No.1 coalface is more than that of the No.3 coalface.

Modified robust finite-horizon filter for discrete-time systems with parameter uncertainties and missing measurements

A robust finite-horizon Kalman filter is designed for linear discrete-time systems subject to norm-bounded uncertainties in the modeling parameters and missing measurements.The missing measurements were described by a binary switching sequence satisfying a conditional probability distribution,the commonest cases in engineering,such that the expectation of the measurements could be utilized during the iteration process.To consider the uncertainties in the system model,an upperbound for the estimation error covariance was obtained since its real value was unaccessible.Our filter scheme is on the basis of minimizing the obtained upper bound where we refer to the deduction of a classic Kalman filter thus calculation of the derivatives are avoided.Simulations are presented to illustrate the effectiveness of the proposed approach.

Adaptive Fault Estimation for Dynamics of High Speed Train Based on Robust UKF Algorithm

This paper proposes an adaptive unscented Kalman filter algorithm(ARUKF)to implement fault estimation for the dynamics of high⁃speed train(HST)with measurement uncertainty and time⁃varying noise with unknown statistics.Firstly,regarding the actuator and sensor fault as the auxiliary variables of the dynamics of HST,an augmented system is established,and the fault estimation problem for dynamics of HST is formulated as the state estimation of the augmented system.Then,considering the measurement uncertainties,a robust lower bound is proposed to modify the update of the UKF to decrease the influence of measurement uncertainty on the filtering accuracy.Further,considering the unknown time⁃varying noise of the dynamics of HST,an adaptive UKF algorithm based on moving window is proposed to estimate the time⁃varying noise so that accurate concurrent actuator and sensor fault estimations of dynamics of HST is implemented.Finally,a five-car model of HST is given to show the effectiveness of this method.