Objective:To develop calibration methods for industrial sensors of dynamic measurements such as acceleration, force and pressure with digital data output (data streams) and internal digital pre-processing, including the extrapolation of the measurement uncertainty from individually calibrated sensors to other individuals of the same type by means of co-calibration and statistical modelling.
The project has so far developed the required basic concepts, terminology and specifications. As a first step, the project developed a micro-controller (µC) board that can hold digital sensors and provides time-stamping traceable to SI. A communication interface based on OPC-Unified Architecture provides data streams from the board to connected PCs and will later be used for the implementation in the project’s testbeds. For the communication of the data from the µC board a collaboration with EMPIR 17IND02 SmartCom has been initiated to develop a joint demonstrator. The software corresponding to the µC board was published on the project’s open source repository on GitHub (https://github.com/Met4FoF).
For the extension of the ZEMA and STRATH testbed, the prototype “Smart-up Unit” is being prepared to integrate MEMS sensors measuring temperature, pressure and acceleration. Collaboration with 17IND02 and the joint Stakeholder Advisory Board (SAB) on the integration of digital communication and digital calibration certificates has been initiated.
Objective: To develop and demonstrate methods enabling digital sensors to provide uncertainty and/or data quality informationtogether with the measurement data.
This project started development of a proof-of-concept “Smart Traceability” sensor by extending a conventional sensor with a “Smart-up Unit”, such that it provides measured values together with their associated uncertainty and other relevant data quality information. Furthermore, the project initiated further development of the software implementations from EMPIR 14SIP08 by extending the corresponding software library PyDynamic. As an initial step, the existing PyDynamic repository was extended to a continuous integration (CI) workflow for automated software quality assurance. The central software repository on GitHub connects PyDynamic and the other software developments from the project to an implementation of the mathematical framework developed in the project. In addition, in collaboration with EMPIR 17IND02 SmartCom and the joint SAB the development of modules for reading and writing digital calibration certificates has been started.
Objective: To develop a cost-efficient in-situ calibration framework for MEMS sensors measuring ambient temperature for their integration into an industrial sensor network with metrological quality infrastructure.
The project has begun the development of an automated testbed equipment (ATE) at the SPEA testbed for traceable in-situ calibration of MEMS temperature sensors using an optimised network of reference sensors in an automated test environment. The initial design of the ATE setup has been drafted and development of the corresponding parts has started. The laboratory calibration facility for the traceable calibration of the on-board temperature sensors of the reference fixture is under construction and an initial version will soon be finished. Data analysis methods will be developed in this project for this novel calibration setup that uses on-board temperature sensors to generate a temperature mapping for the calibration of the MEMS sensors.
Objective: To develop and assess data aggregation methods for industrial sensor networks based on machine learning and efficient software architectures, addressing synchronisation of measurements, making use of redundancies of measurements, taking into account uncertainty from calibration and network communication issues, including strategies for balancing cost versus uncertainty and explore methods to identify the measurement coverage and accuracy required for process output targets.
Generic mathematical models have been derived and potential uncertainty evaluation approaches have been identified. Based on these models, methods are being developed for the transformation of timing and synchronisation issues and use of redundant information. With their implementation in an agent-based framework (ABF), the flexible use of a variety of sensor networks will be achieved. The basic outline of the ABF has been created and its implementation started. In a first step the ABF will then be used to simulate a heterogenous sensor network.
Measurement analysis for sensor networks in the FoF is typically based on ML methods for decision-making. In order to bring metrology into this area, the uncertainty associated with the raw data streams has to be taken into account by the ML methods applied. As an initial step, the methods already in use for the STRATH and ZEMA testbed data are being investigated regarding their extension to take into account measurement uncertainties. In addition, probabilistic ML methods are being evaluated with the data sets so far provided by the project’s testbeds. In order to increase early up-take of these developments, initial data sets have been made publicly available on the Zenodo repository under a dedicated Met4FoF community page (https://zenodo.org/communities/met4fof/. In addition, development of web-based tutorial material for using the data sets has been started. The data sets will be updated with further information and combined with further web-based tutorials on their application with Python-based ML taking into account measurement uncertainties. In addition, it is intended that software releases from the project will be published on the Zenodo platform with an associated persistent identifier as DOI.