Models and Measures in Measurements and Monitoring

Babak, Vitalii; Babak, Serhii V.; Eremenko, Volodymyr; Kuts, Yurii; Myslovych, Mykhailo V.; Scherbak, Leonid; Zaporozhets, Artur

doi:10.1007/978-3-030-70783-5

Cited by 13 publications

(7 citation statements)

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“…In conclusion, adherence to best practices in statistical techniques is essential for achieving reliable and meaningful results in precision metrology. By following established guidelines for data collection and preparation, considering assumptions and considerations in statistical analysis, selecting appropriate models, and effectively interpreting and communicating results, metrologists can enhance the quality and utility of their measurements in various industrial applications (Babak et al .,2021).…”

Section: Best Practices In Statistical Techniques For Precision Metro...mentioning

confidence: 99%

Statistical Techniques in Precision Metrology, Applications and Best Practices

Adeniyi Kehinde Adeleke,

Danny Jose Portillo Montero,

Kehinde Andrew Olu-lawal

et al. 2024

Eng. sci. technol. j.

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Statistical techniques play a pivotal role in precision metrology, ensuring accurate measurements and reliable data analysis in various industries. This review delves into the applications and best practices of statistical techniques in precision metrology, highlighting their significance in enhancing measurement accuracy, reducing uncertainties, and optimizing manufacturing processes. In precision metrology, statistical methods are employed to analyze measurement data, assess measurement uncertainty, and make informed decisions regarding process control and quality assurance. One of the fundamental applications of statistical techniques is in experimental design, where methodologies such as Design of Experiments (DOE) aid in optimizing measurement processes by identifying influential factors and their interactions. Moreover, statistical process control (SPC) techniques like control charts are widely utilized in monitoring and maintaining measurement processes within specified limits, thereby ensuring consistency and reliability in metrology operations. These techniques enable early detection of deviations or anomalies, facilitating timely corrective actions to prevent the production of non-conforming products. Furthermore, the application of statistical analysis in uncertainty evaluation is crucial for quantifying the reliability of measurement results. Techniques such as Monte Carlo simulation and GUM (Guide to the Expression of Uncertainty in Measurement) provide systematic approaches for assessing and expressing measurement uncertainty, considering various sources of error and their contributions to the overall uncertainty budget. In addition to applications, adopting best practices in statistical techniques is essential for ensuring the effectiveness and efficiency of precision metrology processes. This includes proper data collection, adherence to statistical assumptions, selection of appropriate statistical models, and interpretation of results with caution. Overall, this review underscores the importance of statistical techniques in precision metrology, emphasizing their role in improving measurement accuracy, reducing variability, and enhancing overall quality in manufacturing processes. By implementing robust statistical methodologies and adhering to best practices, organizations can achieve higher levels of precision, consistency, and reliability in their metrology operations, ultimately leading to improved product quality and customer satisfaction. Keywords: Statistics, Precision Metrology, Best Practices, Application, Review.

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Section: Best Practices In Statistical Techniques For Precision Metro...mentioning

confidence: 99%

Statistical Techniques in Precision Metrology, Applications and Best Practices

Adeniyi Kehinde Adeleke,

Danny Jose Portillo Montero,

Kehinde Andrew Olu-lawal

et al. 2024

Eng. sci. technol. j.

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show abstract

“…In the case of estimating the parameters of harmonic signals, which are the components of the additive mixture with noise, the problem of processing random phase shifts occurs. Since the models of random angles are similar to the models of random phase shifts [33], the methods of statistical processing of angular data [36], [37] can be applied to them.…”

Section: Probabilistic Space and Model Of Random Angles And Random Ph...mentioning

confidence: 99%

“…The direction of vectors In the space  , we set a random angle as a numerical function     0, 2     . To do this we use the cylindrical coordinates [33]. In the general case, the set of elements in space  has the power of a continuum.…”

Section: Probabilistic Space and Model Of Random Angles And Random Ph...mentioning

confidence: 99%

“…It is used to estimate the measurement uncertainty as a non-negative parameter characterizing the spread of quantity values being attributed to a measurand. To obtain the correct results of the phase measurements, it is necessary to adhere to the concept of harmonization of these measures [33] and to reconcile them with the circular nature of the phase as a measurand.…”

Section: Introductionmentioning

confidence: 99%

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Using Signal Phase in Computerized Systems of Non-destructive Testing

Mei

Kuts

Кочан

et al. 2022

Measurement Science Review

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Phase methods of measuring physical quantities and phase measuring equipment are widely used in various fields of science and technology. The article proposes a signal processing methodology based on a combination of the discrete Hilbert transform (DHT) and deterministic, as well as statistical methods of phase measurement. This methodology makes it possible to more fully use the information resource of the measuring signal phase in a wide range of the signal-to-noise ratio. It can be used both in computerized measurement and testing systems, as well as in the processing of measurement data. The benefits of the DHT are considered. The possibility of using statistics of directional data for phase measurements is shown. Circular statistics, such as the mean ring value, circular variance and the resulting vector length, were proposed for use in the phase measurements. Some examples of the use of this methodology in measurements and non-destructive testing are given.

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“…Important directions of energy informatics are associated with the collection, analysis, deployment, and exploitation of energy status data, modeling, simulation, and prediction of the behavior of energy systems and processes [1], including energy loads and consumption mathematical modeling and computer simulation. Energy resources consumption (e.g., electric, gas, water consumption) analysis and simulation are also important tools for the problems of energy consumption behavior-based user segmentation, electricity consumption pattern (load profiles) analysis, energy consumption forecasting, development of information measurement and information control systems in the electric power industry [4,5].…”

Section: Introductionmentioning

confidence: 99%

Properties of Discrete-Time Conditional Linear Cyclostationary Random Processes in the Problems of Energy Informatics

Fryz¹,

Scherbak²

2023

Sist. dosl. energ. (Online)

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Modern challenges in the energy industry require comprehensive research in the field of energy informatics, which combines computer science, control systems, and energy management systems within a single methodology. An important area of energy informatics is the study of problems of systems and processes modeling in energy, including energy loads and consumption. Linear and conditional linear random processes (CLRP) are mathematical models of signals represented as the sum of a large number of random impulses occurring at random times. The energy consumption, vibration signals of energy objects, etc. can be modeled using this approach. A variant of the CLRP model with discrete time, taking into account the cyclic properties of energy consumption, has been investigated in the paper. The goal is to justify the conditions for the discrete-time CLRP to be a periodically correlated random process, as well as a cyclostationary process. It has been shown that the corresponding conditions depend on the periodicity of the probability distributions of the kernel and the generating white noise of the CLRP representation. To achieve the goal, the properties of mathematical expectation and covariance function of CLRP, as well as the method of characteristic functions, have been used. The paper proves that the discrete-time CLRP is a periodically correlated random sequence if the generating white noise has periodic mathematical expectation and variance, and the kernel is a periodically correlated random field. Based on the analysis of the multivariate characteristic function, it has been proven that the discrete-time CLRP is cyclostationary if the generating white noise is a cyclostationary process and the kernel is a cyclostationary random field. The properties of discrete-time conditional linear cyclostationary random processes are important for mathematical modeling, simulation, statistical analysis, and forecasting of energy consumption. Keywords: mathematical model, energy informatics, conditional linear random process, cyclostationary process, white noise, characteristic function.

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Models and Measures in Measurements and Monitoring

Cited by 13 publications

References 0 publications

Statistical Techniques in Precision Metrology, Applications and Best Practices

Statistical Techniques in Precision Metrology, Applications and Best Practices

Using Signal Phase in Computerized Systems of Non-destructive Testing

Properties of Discrete-Time Conditional Linear Cyclostationary Random Processes in the Problems of Energy Informatics

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