Machine-to-machine systems for data acquisition and measurement with self-validation for the digital metrology transition

IF 5.6 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement Pub Date : 2025-09-26 DOI:10.1016/j.measurement.2025.119149

Gustavo Esteves Coelho, Álvaro Silva Ribeiro, Catarina Simões, Alexandre Pinheiro

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引用次数: 0

Abstract

This paper introduces a methodological framework for developing an autonomous system that minimizes human involvement within a measurement chain. The approach integrates data acquisition, metadata retrieval, and processing within a Machine-to-Machine (M2M) communication paradigm, thereby substantially minimizing manual operations throughout the workflow. To safeguard data integrity and detect unintended corruption during M2M exchanges, a data self-validation mechanism is introduced. Aligned with the Industry 5.0 vision—which emphasizes advanced data management and intelligent machine communication—the proposed approach contributes to the ongoing digital transformation of metrology, namely, through the implementation of Digital Calibration Certificates (DCC), promoting seamless and automated exchange of measurement data. The system was successfully applied to an experimental remote meteorological station, enabling fully autonomous measurement, validation, and data storage using DCC.

查看原文本刊更多论文

机器对机器系统的数据采集和测量与自我验证的数字计量过渡

本文介绍了一种开发自治系统的方法框架，该系统可以最大限度地减少人类在测量链中的参与。该方法在机器对机器（M2M）通信范例中集成了数据采集、元数据检索和处理，从而大大减少了整个工作流程中的人工操作。为了在M2M交换过程中保护数据完整性并检测意外损坏，引入了数据自验证机制。与强调先进数据管理和智能机器通信的工业5.0愿景一致，提议的方法有助于正在进行的计量数字化转型，即通过实施数字校准证书（DCC），促进测量数据的无缝和自动化交换。该系统已成功应用于一个实验性远程气象站，实现了使用DCC的完全自主测量、验证和数据存储。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Measurement 工程技术-工程：综合

CiteScore

10.20

自引率

12.50%

发文量

1589

审稿时长

12.1 months

期刊介绍： Contributions are invited on novel achievements in all fields of measurement and instrumentation science and technology. Authors are encouraged to submit novel material, whose ultimate goal is an advancement in the state of the art of: measurement and metrology fundamentals, sensors, measurement instruments, measurement and estimation techniques, measurement data processing and fusion algorithms, evaluation procedures and methodologies for plants and industrial processes, performance analysis of systems, processes and algorithms, mathematical models for measurement-oriented purposes, distributed measurement systems in a connected world.