Smart Metrology

Smart Metrology is a modern approach to industrial metrology. The name was introduced by Jean-Michel Pou and Laurent Leblond, a French meteorologist and a French statistician. The term was coined in their book, La Smart Metrology: De la métrologie des instruments à la métrologie des décisions.[1] It was then adopted by Deltamu, a French company providing services in the field of industrial metrology.

The approach promoted by Smart Metrology applies the exploitation of data and information, including that provided by big data,[2] to implement an approach based on the three pillars of metrology[3] (uncertainty,[4] calibration and traceability) in industrial applications.[5]

## Contents

* 1 Approach
* 1.1 Innovation
* 2 See also
* 3 References

## Approach

The approach suggested by Smart Metrology is fully framed within the ISO 9001 recommendations. Usual metrology is often regarded as a pure cost and is actually not following the ISO 9001 quality standards.

### Innovation

Smart Metrology[1] follows a different approach according to the following steps:

* The measuring equipment is monitored using historical and relevant data to detect whether a doubt exists. If such a doubt exists, the equipment is calibrated.
* The available (a priori) information is used by applying advanced statistical approaches, such as Bayesian inference, for monitoring and is used in the decision-making process.[6][7]
* The calibration intervals were not at fixed intervals.

## See also

Main page: Outline of metrology and measurement

* Dimensional metrology
* Forensic metrology
* Time metrology
* Quantum metrology

## References

1. ↑ 1.0 1.1 Pou, Jean-Michel. (2016). La smart metrology : de la métrologie des instruments à la métrologie des décisions. Leblond, Laurent, (19 ... – ... ; expert en statistique industrielle), Nordon, Didier.. La Plaine Saint-Denis: AFNOR Editions. ISBN 978-2-12-465545-8. OCLC 952466728. https://www.worldcat.org/oclc/952466728.
2. ↑ Mari, Luca; Petri, Dario (2017). "The metrological culture in the context of big data: managing data-driven decision confidence". IEEE Instrumentation & Measurement Magazine 20 (5): 4–20. doi:10.1109/MIM.2017.8036688. ISSN 1094-6969. https://ieeexplore.ieee.org/document/8036688.
3. ↑ Ferrero, Alessandro (2015). "The pillars of metrology". IEEE Instrumentation & Measurement Magazine 18 (6): 7–11. doi:10.1109/MIM.2015.7335771. ISSN 1094-6969. https://ieeexplore.ieee.org/document/7335771.
4. ↑ JCGM 100:2008, Evaluation of measurement data — Guide to the expression of uncertainty in measurement, 2008, https://www.bipm.org/utils/common/documents/jcgm/JCGM_100_2008_E.pdf
5. ↑ Lazzari, Annarita; Pou, Jean-Michel; Dubois, Christophe; Leblond, Laurent (2017). "Smart metrology: the importance of metrology of decisions in the big data era". IEEE Instrumentation & Measurement Magazine 20 (6): 22–29. doi:10.1109/MIM.2017.8121947. ISSN 1094-6969. https://ieeexplore.ieee.org/document/8121947.
6. ↑ Pou, Jean-Michel; Leblond, Laurent (2018). "ISO / IEC guide 98-4: A copernican revolution for metrology". IEEE Instrumentation & Measurement Magazine 21 (5): 6–10. doi:10.1109/MIM.2018.8515699. ISSN 1094-6969. https://ieeexplore.ieee.org/document/8515699.
7. ↑ Ferrero, Alessandro; Salicone, Simona; Jetti, Harsha Vardhana (2019). "Bayesian approach to uncertainty evaluation: Is it always working?" (in en). 19th International Congress of Metrology (CIM2019). EDP Sciences. pp. 16002. doi:10.1051/metrology/201916002. ISBN 978-2-7598-9069-9. https://cfmetrologie.edpsciences.org/articles/metrology/abs/2019/01/metrology_cim2019_16002/metrology_cim2019_16002.html.

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