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This paper concerns with the measurement uncertainty estimation in the analog‐to‐digital conversion‐based instruments. By using an ad hoc developed software tool, the Monte Carlo method is applied in order to assess the uncertainties associated with the measurement results, overcoming the possible inapplicability of the pure theoretical approach prescribed in the ISO – “Guide to the Expression of Uncertainty in Measurement”. By implementing the software tool in the measurement instruments, the proposed approach can be utilized in order to make the instrument itself able to auto‐estimate the measurement uncertainties.

The assessment of the quality of the electric power supply, as well as that of the electric loads, is becoming a critical problem, especially when the liberalization of the electricity market is involved. Power quality can be evaluated by means of a number of quantities and indices whose measurement is not straightforward and is generally attained by means of digital signal processing techniques based on complex algorithms. The assessment of the uncertainty of the results of such measurements is a critical, open problem. This paper proposes a general purpose approach, based on the Monte Carlo method that, starting from the estimated contributions to the uncertainty of each device in the measurement chain, estimates the probability density distribution of the measurement result, and therefore, its standard uncertainty. This approach has been experimentally validated for the active power measurement and applied to the estimation of the uncertainty of the measurement of more complex power quality indices.

The assumption that a set of observed variables is a function of an underlying common factor plus some error has dominated measurement in marketing and the social sciences in general for decades. This view of measurement comes with assumptions, which, however, are rarely discussed in research. In this article, we question the legitimacy of several of these assumptions, arguing that (1) the common factor model is rarely correct in the population, (2) the common factor does not correspond to the quantity the researcher intends to measure, and (3) the measurement error does not fully capture the uncertainty associated with measurement. Our discussions call for a fundamental rethinking of measurement in the social sciences. Adapting an uncertainty-centric approach to measurement, which has become the norm in in the physical sciences, offers a means to address the limitations of current measurement practice in marketing.

The determination of the measurement uncertainty is relevant for all measurement processes. In production engineering, the measurement uncertainty needs to be known to avoid erroneous decisions. However, its determination is associated to high effort due to the expertise and expenditure that is needed for modelling measurement processes. Once a measurement model is developed, it cannot necessarily be used for any other measurement process. In order to make an existing model useable for other measurement processes and thus to reduce the effort for the determination of the measurement uncertainty, a procedure for the migration of measurement models has to be developed.

This paper presents an approach to migrate measurement models from an old process to a new “similar” process. In this approach, the authors first define “similarity” of two processes mathematically and then use it to give a first estimate of the measurement uncertainty of the similar measurement process and develop different learning strategies. A trained machine-learning model is then migrated to a similar measurement process without having to perform an equal size of experiments.Similarity assessment and model migration

The authors’ findings show that the proposed similarity assessment and model migration strategy can be used for reducing the effort for measurement uncertainty determination. They show that their method can be applied to a real pair of similar measurement processes, i.e. two computed tomography scans. It can be shown that, when applying the proposed method, a valid estimation of uncertainty and valid model even when using less data, i.e. less effort, can be built.

The proposed strategy can be applied to any two measurement processes showing a particular “similarity” and thus reduces the effort in estimating measurement uncertainties and finding valid measurement models.

The purpose of this paper is to increase the measurement accuracy of assembly deviations of an inertial navigation system, a new evaluation and optimal method of assembly metrology system is proposed, which takes into account the uncertainty from laser tracker hardware and coordinate system transformation, and is based on the Monte Carlo method.

The uncertainty model of the laser tracker is established and its parameters are obtained from the known repeated test data by kriging interpolation and the least squares method. The errors of coordinate transformation are reduced by using a weighted point matching method, and the uncertainty of the transformation parameters is obtained based on the generalized inverse theory. The weighting coefficients of each reference point are optimized by the particle swarm optimization method according to the assembly requirements.

The experiment results show that measurement error and predicted results match well, and the assembly deviation uncertainty of large component is reduced by about 10 per cent compared with the singular value decomposition method.

This paper proposes a method to evaluate and eliminate the influence of random errors of the laser tracker during evaluation process of coordinate translation parameters and assembly deviations. The proposed method would be useful to improve the assembly measurement accuracy through less measurement times.

Measurement uncertainty calculation is an important and complicated problem in digitised components inspection. In such inspections, a coordinate measuring machine (CMM) and laser scanner are usually used to get the surface point clouds of the component in different postures. Then, the point clouds are registered to construct fully connected point clouds of the component’s surfaces. However, in most cases, the measurement uncertainty is difficult to estimate after the scanned point cloud has been registered. This paper aims to propose a simplified method for calculating the uncertainty of point cloud measurements based on spatial feature registration.

In the proposed method, algorithmic models are used to calculate the point cloud measurement uncertainty based on noncontact measurements of the planes, lines and points of the component and spatial feature registration.

The measurement uncertainty based on spatial feature registration is related to the mutual position of registration features and the number of sensor commutation in the scanning process, but not to the spatial distribution of the measured feature. The results of experiments conducted verify the efficacy of the proposed method.

The proposed method provides an efficient algorithm for calculating the measurement uncertainty of registration point clouds based on part features, and therefore has important theoretical and practical significance in digitised components inspection.

Measurement uncertainty is present in all measurement processes in the field of production engineering. However, this uncertainty should be minimized to avoid erroneous decisions. Present methods to determine the measurement uncertainty are either only applicable to certain processes and do not lead to valid results in general or require a high effort in their application. To optimize the costs and benefits of the measurement uncertainty determination, a method has to be developed which is valid in general and easy to apply. The paper aims to discuss these issues.

This paper presents a new technique for determining the measurement uncertainty of complex measurement processes. The approximation capability of artificial neural networks with one hidden layer is proven for continuous functions and represents the basis for a method for determining a measurement model for continuous measurement values.

As this method does not require any previous knowledge or expertise, it is easy to apply to any measurement process with a continuous output. Using the model equation for the measurement values obtained by the neural network, the measurement uncertainty can be derived using common methods, like the Guide to the expression of uncertainty in measurement. Moreover, a method for evaluating the model performance is presented. By comparing measured values with the output of the neural network, a range in which the model is valid can be established. Combining the evaluation process with the modelling itself, the model can be improved with no further effort.

The developed method simplifies the design of neural networks in general and the modelling for the determination of measurement uncertainty in particular.

This study examines (1) the extent of key audit matters (KAMs) reported by auditors is related to accounting estimates, (2) whether measurement uncertainty and management bias affect auditors to do so and (3) whether the use of accounting estimates, given the measurement uncertainty and management bias reported in KAMs adversely affects the decision usefulness of accounting information.

Data on key audit matters, accounting estimates, measurement uncertainty, management bias, etc. were collected from the auditor's reports of 351 sample Chinese listed firms. It employs regression analyses to assess the hypotheses on issues affecting the report of these key audit matters and the impacts on the decision usefulness of accounting information.

Fair value and impairment loss estimations make up of 2.6 and 44.1% of the 606 KAMs identified, respectively. Measurement uncertainty is positively, while management bias is negatively, affecting auditors report KAMs related to accounting estimates. The use of accounting estimates in firms where their auditors reported the KAMs related to accounting estimates does not enhance the value and predictive relevance of reported earnings. The assurance works on, and reporting of, KAMs served as a “red flag” about the accounting estimates.

The use of accounting estimates does not always lead to enhanced decision-useful accounting information. Auditors, in their stewardship role, shall ensure that the measurement uncertainty issue is appropriately identified, addressed and verified. In addition, they shall provide an effective check-and-balance to the accounting discretion managers have in providing decision-useful information from opportunistic reporting.

This study examines the proposition that while the use of estimates can enhance the decision usefulness of accounting information, it can also induce measurement uncertainty and management bias into financial reporting.

For the measurement of large-scale components in aircraft assembly, the evaluation of coordinate transformation parameters between the coordinate frames of individual measurement systems to the assembly frame is an essential task, which is usually completed by registration of the enhanced reference system (ERS) points. This paper aims to propose an analytical method to evaluate the uncertainties of transformation parameters considering both the measurement error and the deployment error of ERS points.

For each measuring station, the measured coordinates of ERS points are first roughly registered to the assembly coordinate system using the singular value decomposition method. Then, a linear transformation model considering the measurement error and deployment error of ERS points is developed, and the analytical solution of transformation parameters’ uncertainties is derived. Moreover, the covariance matrix of each ERS points in the transformation evaluation is calculated based on a new uncertainty ellipsoid model and variance-covariance propagation law.

For the transformation of both single and multiple measuring stations, the derived uncertainties of transformation parameters by the proposed analytical method are identical to that obtained by the state-of-the-art iterative method, but the solution process is simpler, and the computation expenses are much less.

The proposed uncertainty evaluation method would be useful for in-site measurement and optimization of the configuration of ERS points in the design of fixture and large assembly field. It could also be applied to other registration applications with errors on both sides of registration points.

This paper deals with the uncertainty in digital measurement systems designed for power quality applications. The main goal of this work is to evaluate such uncertainty by means of a Monte Carlo method recently proposed in the literature. The accuracy of the measurement result obtained with a DSP‐based instrument for power quality metering depends on the behavior of the devices located in both the conditioning block and A/D conversion stage: it is thus necessary to consider the uncertainties introduced by each component of the system and the propagation of their effects through the measurement chain. Here, the uncertainty is estimated starting from the technical specifications provided by the manufacturers of these devices. Experimental results are reported to show the importance of some concerns about the practical implementation of the proposed methodology in a real instrument.