Dynamic Measurement Uncertainty Research Articles

Dynamic modeling and performance evaluation of piezoelectric impact drive system based on neural network

In metrology and industrial design, the evaluation of measurement uncertainty and error is crucial to the measurement process. The Guide to the Expression of Uncertainty in Measurement and its supplementary documents have established a unified framework and standard for evaluating measurement uncertainty. However, a reasonable method for evaluating dynamic measurement uncertainty has not yet been proposed. By analyzing the dynamic measurement system, and using the long short-term memory time neural network to model the nonlinear dynamics represented by a piezoelectric drive platform, this paper evaluates the system’s dynamic measurement uncertainty through deep integration methods. Bayesian theory is used to propagate probability densities, and experimental results demonstrate the effectiveness of this method for assessing dynamic measurement uncertainty.

A self-adaptive method for the assessment of dynamic measurement uncertainty

Measurement uncertainty is as important as measurement in metrology and industry. The GUM and its supplements provide a widely accepted framework for evaluating measurement uncertainty; but don’t provide a reasonable assessment method for some special circumstances, especially for dynamic measurement. Several emerging methodologies with different mathematical approaches are used for evaluating the dynamic uncertainty in a specific application, such as knowing the characteristics of data. To expand the applicability, a self-adaptive method is proposed. This method evaluates measurement uncertainty by analyzing the compositions of dynamic data, regardless of linearity, stationarity, or stochasticity. Information entropy on spectra combined with EDM algorithms is presented to divide dynamic data into deterministic and stochastic components; and then a Bayesian model and a time-varying auto-regression model are used to analyze decomposed components, respectively. Synthetic noisy signals and experimental data from a double-rotor table are utilized to demonstrate the effectiveness of the proposed method.

Design of MultiChannel Parallel Dynamic Testing Virtual Instrument Based on Dynamic Measurement

With the rapid advancement of virtual instrument technology, converting the data measurement and control platform to a virtual one has become fashionable. This study focuses on dynamic measurement in order to create a multichannel parallel dynamic testing virtual instrument and then test the instrument’s performance. System login, user management, database storage, query and compression, data processing and real-time display, multifunctional analysis, remote access, and report generation modules have all been designed and implemented. A dynamic measurement uncertainty evaluation method based on quasi-Monte Carlo (MC) method is proposed in light of the common phenomenon that static is used instead of dynamic in the current evaluation process of dynamic measurement uncertainty. For this example, the actual results show that the algorithm produces very reliable and stable calculation results.

A Self-Adaptive Method for the Assessment of Dynamic Measurement Uncertainty

A Self-Adaptive Method for the Assessment of Dynamic Measurement Uncertainty

Gas Sensor Array Dynamic Measurement Uncertainty Evaluation and Optimization Algorithm

Metal oxide semiconductor (MOS) gas sensor array dynamic measurement uncertainty evaluation, which currently mainly uses static measurement approximation estimations, cannot accurately distinguish the measured value of the sudden change of the measured value caused by the dynamic mutation of the measured gas or the actual sensor array local fault caused by the sudden change of the measured value, thereby resulting in a dynamic measurement process of the MOS gas sensor array uncertainty evaluation results and the validation measurement value reliability being greatly reduced. This paper presents a dynamic adaptive Kalman filter andGray bootstrap comprehensive modified prediction model for the dynamic measurement uncertainty evaluation. The dynamic adaptive Kalman filter and Gray bootstrap method is used to estimate the good performance of the probability distribution function of the measured value, and the uncertainty evaluation of the dynamic measurement state of the MOS gas sensor array is realized. Using the correlation of the dynamic adaptive Kalman filter and Gray model multisensor output, a new MOS gas sensor array measurement value confirmation algorithm is proposed to distinguish the measured value mutation caused by the normal dynamic mutation of the measured gas and the fault of the real sensor array. Finally, the MOS gas sensor array measurement is set up, and the experiments show that the proposed MOS gas sensor array dynamic measurement uncertainty evaluation and an optimization algorithm is effective.

E-commerce Service Equilibrium Prediction Simulation Based on Ontology

In the process of e-commerce service users to accurately forecast demand, can expand electricity service scope, improve the quality of electricity services. In this paper, a process of e-commerce service based on ontology user demand equilibrium prediction method. First build electric company service equilibrium recommend user ontology, calculate the semantic similarity between ontology, have similar collections, on the basis of general prediction to target users near to the object as a group, the forecast probability calculation of high trust subgroup, balanced by the dynamic measurement uncertainty neighbor prediction, effectively completed the demand for electricity service user equilibrium predictions. Simulation experiments show that the proposed method can effectively improve electricity service users demand forecasting accuracy, strong use value.

Multi-dimensional dynamic measurement uncertainty analysis for the six-axis force sensor used in docking system

The tough space environment has a great influence on the reliability of the measurement results owing to the rapidly aging sensor. Also, the measurement uncertainty caused by the random factors seriously affects the accuracy of the sensor. Aiming at the multidimensional dynamic uncertainty problem of the six-axis force measurement, a six-axis force sensor for docking system is presented based on the piezoelectric force sensing element. The decoupling algorithm of the six-axis force sensor is proposed. Grey prediction model is employed to establish the dynamic uncertainty model in single channel. Residual test, correlation test and posterior error test are conducted to verify the reliability of the prediction model. The multidimensional dynamic uncertainty model of the six-axis force sensor is derived by combination of the decoupling algorithm and the dynamic uncertainty in single channel. The multidimensional dynamic uncertainty model of the six-axis force sensor can avoid the error signals for the force feedback control in the space environment, and the model can also be employed for the measurement of six-axis force in any other harsh conditions.

Evaluation of dynamic measurement uncertainty – an open-source software package to bridge theory and practice

Abstract. The analysis of dynamic measurements provides numerous challenges that significantly limit the use of existing calibration facilities and mathematical methodologies. For instance, dynamic measurement analysis requires the application of methods from digital signal processing, system and control theory, and multivariate statistics. The design of digital filters and the corresponding evaluation of measurement uncertainty for high-dimensional measurands are particularly challenging. Several international research projects involving national metrology institutes (NMIs), academia and industry have developed mathematical, statistical and technical methodologies for the treatment of dynamic measurements at NMI level. The aim of the European research project 14SIP08 is the development of guidelines and software to extend the applicability of those methodologies to a wider range of users. This paper outlines the required activities towards a traceability chain for dynamic measurements from NMIs to industrial applications. A key aspect is the development and provision of a new open-source software package. The software is freely available, open for non-commercial distribution, and contains the most important data analysis tools for dynamic measurements.

Evaluation of dynamic measurement uncertainty in the time domain in the application to high speed rotating machinery

This article proposes a spectral method of assessing the dynamic uncertainty of measurement devices, that allows the investigation of the accuracy of measurement in dynamic operating conditions in the frequency domain and the evaluation of the amplitude values of dynamic uncertainty based on input signal frequency characteristics and spectral function. The results were tested when evaluating the dynamic uncertainty in measurements of the vibration acceleration of an engine. It was established that the maximum value of the dynamic uncertainty of the measurement of the vibration acceleration stands at 0.14 m/s2 for an observation time of 300 s and with a nominal value of the signal of vibration acceleration of 2.34 m/s2 at a frequency of 6 kHz, which in relative units is 6%.

Calibration and uncertainty analysis for multicomponent force/torque measurements

Abstract Many applications in research and industry require accurate measurements of force and torque vectors G. Schicker, R. Wegener. Drehmoment richtig messen. Hottinger-Baldwin-Messtechnik, 2002; R. Schwartz. Teil B: Sensoren, Kraft, Masse, Drehmoment. In H.-J. Gevatter and U. Grünhaupt, editors, Handbuch der Mess- und Automatisierungstechnik in der Produktion, VDI-Buch, pages 55–92. Springer, Dordrecht, 2006. as well as their time dependency. This measurement task can be solved by using multicomponent sensors. Despite of their wide distribution there is a lack of official guides for their calibration D. Schwind and H. Raabe. A new Calibration Procedure for Multicomponent transducers. XX IMEKO World Congress, Metrology for Green Growth, Busan, Republic of Korea, September 9–12, 2012. and methods for evaluation of measurement uncertainty. In many applications statically calibrated mulicomponent sensors are used for measurement of dynamic quantities Y.-K. Park, R. Kumme, and D.-I. Kang. Dynamic investigation of a binocular six-component force-moment sensor. Measurement Science and Technology, 13(8):1311–1318, 2002. which causes severe deviations in measurement when the quantity to be measured has a frequency near or above resonance frequency of the transducer. This paper describes the static calibration and a method for uncertainty analysis using the example of a six-component sensor for force and torque. Subsequently the dynamic calibration of such a sensor is discussed. Based on the results of the dynamic calibration an approach for evaluation of dynamic measurement uncertainty is applied.

Dynamic measurements and uncertainty estimation of clinical thermometers using Monte Carlo method

Clinical thermometers in intensive care units are used for the continuous measurement of body temperature. This study describes a procedure for dynamic measurement uncertainty evaluation in order to examine the requirements for clinical thermometer dynamic properties in standards and recommendations. In this study thermistors were used as temperature sensors, transient temperature measurements were performed in water and air and the measurement data were processed for the investigation of thermometer dynamic properties. The thermometers were mathematically modelled. A Monte Carlo method was implemented for dynamic measurement uncertainty evaluation. The measurement uncertainty was analysed for static and dynamic conditions. Results showed that dynamic uncertainty is much larger than steady-state uncertainty. The results of dynamic uncertainty analysis were applied on an example of clinical measurements and were compared to current requirements in ISO standard for clinical thermometers. It can be concluded that there was no need for dynamic evaluation of clinical thermometers for continuous measurement, while dynamic measurement uncertainty was within the demands of target uncertainty. Whereas in the case of intermittent predictive thermometers, the thermometer dynamic properties had a significant impact on the measurement result. Estimation of dynamic uncertainty is crucial for the assurance of traceable and comparable measurements.

Status Self-Validation of Sensor Arrays Using Gray Forecasting Model and Bootstrap Method

The reliability monitoring of sensor arrays is a challenging and critical issue that directly influences the performance of a measurement and control system. In this paper, a novel strategy based on gray forecasting model GM(1,1) coupled with the bootstrap method is proposed for status self-validation of sensor arrays. The proposed strategy focuses on fault detection, isolation, and recovery (FDIR), data validation and dynamic measurement uncertainty estimation of the sensor arrays. The FDIR scheme can effectively detect and isolate sensor abrupt faults and simultaneously accomplish fault recovery with high accuracy and good timeliness. Furthermore, the proposed FDIR scheme has the advantage of discriminating between fault-free signals with sudden changes and undoubted abrupt faults through the trust mechanism. The model GM(1,1) is updated continuously by a metabolism method to improve the adaptivity of the strategy for reliability monitoring. After signal reconstruction, the data validation and dynamic measurement uncertainty can be evaluated by the bootstrap method without any prior information about measurands. A real metal-oxide gas sensor array experimental system is designed to verify the excellent performance of the proposed strategy. The experimental results demonstrate that the proposed approach is capable of conducting the status self-validation of sensor arrays effectively and improving the reliability of sensor arrays in engineering applications.

Technique Of Research Uncertainty Dynamic Measurements Of Vibration Acceleration Of Rotating Machines

Аabstract: Developed methodology for estimation of dynamic measurement uncertainty, which corresponds to the international standards for evaluating the quality of measurements - the concept of uncertainty. It is proposed to estimate the uncertainty of dynamic measurements on the basis of the frequency characteristic measuring means and the spectral function of the input signal. This method was tested in the evaluation of the dynamic measurement of vibration of rotating machines that helped confirm its correctness and efficiency. It is found that the maximum value of the dynamic uncertainty vibration acceleration measurement of 0,05 m/s 2 for an observation time of 300 s and the nominal value signal of vibration acceleration 0.35 m/s 2 at a frequency of 6 kHz.

A New Method for Partial Discharge Signal Processing

A partial discharge (PD) signal processing method based on dynamic measurement theory and wavelet transform is proposed in this paper. The deterministic component was separated by polynomial fitting, and the random component of the remaining residual after the separation was estimated using autoregressive (AR) model; The true value estimate and dynamic measurement uncertainty of noisy signal were obtained by the deterministic component and random component;Db8 wavelet and the soft threshold based on Stein’s Unbiased Estimate of Risk were used to smoothly denoise for better PD signal processing. Finally, the effectiveness of the method was verified by MATLAB simulation and experimental noisy PD signal extraction.

Multi-target Bayesian filter for propagating marginal distribution

The Bayesian filter and its approximation, the probability hypothesis density (PHD) filter, propagate joint distribution of the multi-target state and the first-order moment of the joint distribution, respectively. However, these two filters fail to distinguish multiple distinct targets when these targets are closely spaced. To efficiently distinguish closely spaced targets according to a sequence of measurements, we (1) use the individual state distributions to model the uncertainties of individual target states caused by the target dynamic uncertainty and measurement uncertainty, (2) use the existence probabilities of individual targets to characterize the randomness of target appearance and disappearance, and (3) propose a novel multi-target Bayesian filter. Instead of maintaining the joint state distribution, the proposed filter jointly propagates the marginal distributions and existence probabilities of each target. An implementation of the proposed filter for linear and Gaussian models is also presented to deal with an unknown and variable number of targets. The simulation results demonstrate that the proposed filter is better at distinguishing distinct targets and tracking multiple targets than the Gaussian mixture PHD filter.

Dynamic Range Analysis of the Phase Generated Carrier Demodulation Technique

The dependence of the dynamic range of the phase generated carrier (PGC) technique on low-pass filters passbands is investigated using a simulation model. A nonlinear character of this dependence, which could lead to dynamic range limitations or measurement uncertainty, is presented for the first time. A detailed theoretical analysis is provided to verify the simulation results and these results are consistent with performed calculations. The method for the calculation of low-pass filters passbands according to the required dynamic range upper limit is proposed.

The dynamic measurement uncertainty evaluation on coal mine pump performance testing systems

In view of applying the static measurement theory to evaluate the measurement uncertainty of a testing system, this paper develops a comprehensive coal mine pump performance testing system based on virtual instruments, and applies unascertained theory to evaluate the system's dynamic measurement uncertainty. Compared with the traditional measurement uncertainty of A kind and B kind, this method is without the limitation of the direct measurement, and the question condition and the evaluation better fit reality.

Propagation of dynamic measurement uncertainty

The time-dependent measurement uncertainty has been evaluated in a number of recent publications, starting from a known uncertain dynamic model. This could be defined as the ‘downward’ propagation of uncertainty from the model to the targeted measurement. The propagation of uncertainty ‘upward’ from the calibration experiment to a dynamic model traditionally belongs to system identification. The use of different representations (time, frequency, etc) is ubiquitous in dynamic measurement analyses. An expression of uncertainty in dynamic measurements is formulated for the first time in this paper independent of representation, joining upward as well as downward propagation. For applications in metrology, the high quality of the characterization may be prohibitive for any reasonably large and robust model to pass the whiteness test. This test is therefore relaxed by not directly requiring small systematic model errors in comparison to the randomness of the characterization. Instead, the systematic error of the dynamic model is propagated to the uncertainty of the measurand, analogously but differently to how stochastic contributions are propagated. The pass criterion of the model is thereby transferred from the identification to acceptance of the total accumulated uncertainty of the measurand. This increases the relevance of the test of the model as it relates to its final use rather than the quality of the calibration. The propagation of uncertainty hence includes the propagation of systematic model errors. For illustration, the ‘upward’ propagation of uncertainty is applied to determine if an appliance box is damaged in an earthquake experiment. In this case, relaxation of the whiteness test was required to reach a conclusive result.

A novel method of evaluating dynamic measurement uncertainty utilizing digital filters

For every measurement, a measurement uncertainty should be associated with the estimate of the measurand. In particular, this applies to all the common non-stationary transient dynamic measurements made with linear time-invariant measurement systems, despite the present standard treatment providing little guidance for this case. To complete recent studies on the estimation and correction of the systematic dynamic error, the remaining evaluation of measurement uncertainty is addressed here. A method based on digital filtering will be utilized to find the generically time-dependent dynamic contribution to the uncertainty. It will be shown that the widely accepted approach to calculate the stationary uncertainty can be extended to transient measurements, provided some complementary techniques are used and the sensitivity is generalized to be a time-dependent signal instead of a constant. For illustration, the method is applied to the same measurement system analysed in the related published studies.

Uncertainty evaluation for dynamic measurements modelled by a linear time-invariant system

Evaluation of measurement uncertainty is considered when the value of the measurand depends on the continuous variable time. A concept of dynamic measurement uncertainty is introduced by generalizing the GUM approach. The concept is applied to linear and time-invariant systems which are often appropriate to model dynamic measurements. Digital filtering is proposed for estimating the time-dependent value of the measurand and the design of an appropriate FIR filter is described. Dynamic uncertainty evaluation is then carried out for this analysis and conditions are specified for its proper use. The approach is illustrated for the particular example of a second-order model. It is shown in terms of simulations that the proposed analysis yields significantly improved results when compared with the sometimes applied quasi-static treatment.