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]
Approach
[edit]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
[edit]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
[edit]References
[edit]- ^ a b 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.
- ^ 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. S2CID 19784029.
- ^ Ferrero, Alessandro (2015). "The pillars of metrology". IEEE Instrumentation & Measurement Magazine. 18 (6): 7–11. doi:10.1109/MIM.2015.7335771. hdl:11311/984646. ISSN 1094-6969. S2CID 20051541.
- ^ 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
- ^ 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. S2CID 22216034.
- ^ 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. S2CID 53232687.
- ^ Ferrero, Alessandro; Salicone, Simona; Jetti, Harsha Vardhana (2019). "Bayesian approach to uncertainty evaluation: Is it always working?". In Gazal, Sandrine (ed.). 19th International Congress of Metrology (CIM2019). EDP Sciences. p. 16002. doi:10.1051/metrology/201916002. ISBN 978-2-7598-9069-9.
Text is available under the CC BY-SA 4.0 license; additional terms may apply.
Images, videos and audio are available under their respective licenses.
