Review of Theoretical and Applied Research on Measurement Uncertainty

TAO Meng; REN Siyuan; LAO Changjuan

doi:10.12338/j.issn.2096-9015.2023.0263

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TAO Meng, REN Siyuan, LAO Changjuan. Review of Theoretical and Applied Research on Measurement Uncertainty[J]. Metrology Science and Technology. doi: 10.12338/j.issn.2096-9015.2023.0263

Citation:

TAO Meng, REN Siyuan, LAO Changjuan. Review of Theoretical and Applied Research on Measurement Uncertainty[J]. Metrology Science and Technology. doi: 10.12338/j.issn.2096-9015.2023.0263

TAO Meng, REN Siyuan, LAO Changjuan. Review of Theoretical and Applied Research on Measurement Uncertainty[J]. Metrology Science and Technology. doi: 10.12338/j.issn.2096-9015.2023.0263

Citation:

TAO Meng, REN Siyuan, LAO Changjuan. Review of Theoretical and Applied Research on Measurement Uncertainty[J]. Metrology Science and Technology. doi: 10.12338/j.issn.2096-9015.2023.0263

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Review of Theoretical and Applied Research on Measurement Uncertainty

doi: 10.12338/j.issn.2096-9015.2023.0263

National Institute of Metrology, Beijing 100029, China

Received Date: 2023-11-08
Accepted Date: 2023-12-25
Rev Recd Date: 2024-03-20

Available Online: 2024-04-12

Abstract

Abstract

This article provides a systematic review of the theory, research and application on measurement uncertainty. Firstly, it gives an overall introduction to the historical development. Secondly, the basic principles, latest researches and limitations of several types of mainstream measurement uncertainty evaluation methods are summarized, such as the GUM method, which is the most commonly used evaluation method at present, mainly for the linear or approximate linear measurement models and adopts the method based on the transfer of standardized uncertainty; the Monte Carlo-based measurement uncertainty evaluation method and its derivatives, the proposed Monte Carlo method and the adaptive Monte Carlo method have wider applicability when dealing with complex models; Bayes-based measurement uncertainty assessment method can give full play to the value of the a priori data in small-sample measurements and has a good performance. In addition, the article discusses some methods usually used under information scarcity, such as grey evaluating method, fuzzy evaluating method, maximum entropy method, and neural network method. Finally, the article provides a brief summary of various evaluating methods and believes that with the development of artificial intelligence technology, methods such as support vector machines and neural networks have broad prospects for application in complex measurement models and measurement environments.
- metrology,
- measurement uncertainty,
- GUM,
- Monte Carlo,
- Bayesian

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References(72)

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