Sensor information as a service – component of networked production
-
Published:2018-05-16
Issue:1
Volume:7
Page:389-402
-
ISSN:2194-878X
-
Container-title:Journal of Sensors and Sensor Systems
-
language:en
-
Short-container-title:J. Sens. Sens. Syst.
Author:
Schmitt Robert H.,Voigtmann Christoph
Abstract
Abstract. Metrology has a key position in networked, adaptive
production, with the task of a holistic and valid assessment of the state of various
production scenarios. With the diminishing focus on a device-specific
development towards an adaptive production network, which is less
hierarchical in the sense of the “Internet of production”, and with the focus on
the properties of cyber-physical systems (CPSs), new opportunities for
the strengthening of metrology arise. Characteristic of these CPSs are
sensors for multi-modal data acquisition, actuators for interaction with the
environment, distributed computing power and the ability to spontaneously or
permanently network itself. They form the basis for the creation of a
“digital shadow” and thus are essential components of a model for process
control. Current trends and challenges for metrology in networked
production, such as multi-sensor systems, model-based measurements, virtual
measurement processes or the integration into adaptable production systems,
broaden the boundaries of future requirements of metrology, in particular
with regard to its flexibility, speed and compatibility. A prerequisite is
a scalable, specifiable information fusion. A solution to this is the
service-based provision of sensor information, measurement data and
decisions, which can be flexibly adapted to task-specific requirements. For
this concept of “sensor information as a service”, development stages and
prerequisites for its implementation as well as affected areas are discussed.
Publisher
Copernicus GmbH
Subject
Electrical and Electronic Engineering,Instrumentation
Reference97 articles.
1. Aktakka, E. E. and Najafi, K.: A six-axis micro platform for in situ calibration of MEMS inertial sensors, in: 2016 IEEE 29th International Conference on Micro Electro Mechanical Systems (MEMS), 24–28 January 2016, Shanghai, China, 243–246, 2016. 2. Altintas, Y., Brecher, C., Weck, M., and Witt, S.: Virtual Machine Tool, CIRP Ann.-Manuf. Techn., 54, 115–138, https://doi.org/10.1016/S0007-8506(07)60022-5, 2005. 3. Barnaghi, P., Meissner, S., Presser, M., and Moessner, K.: Sense and Sens'ability: Semantic Data Modelling for Sensor Networks, ICT-MobileSummit 2009 Conference Proceedings, 10–12 June 2009, Santander, Spain, 2009. 4. Bauernhansl, T., Krüger, J., Reinhart, G., and Schuh, G.: Wgp-Standpunkt Industrie 4.0, available at: https://wgp.de/wp-content/uploads/WGP-Standpunkt_Industrie_4-0.pdf, last access: 12 January 2017. 5. Bell, M.: Service-Oriented Modeling, John Wiley & Sons, Inc, Hoboken, NJ, USA, 2012.
Cited by
15 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|