This page provides a brief overview of the software created during the project. It is made available open source and free of charge primarily on GitHub and various other platforms. For instance, we generate our DOIs with the help of Zenodo and provide our container images using Docker Hub. That serves others to use and benefit from the project outcomes. For more information, see the respective repositories linked below.
This codebase combines all the code written for or used in the EMPIR project 17IND12 Metrology for the Factory of the Future to enable pulling/cloning all the code and all coding related documents at once.
The following sections on this page list the most important of the contained software projects and link to their latest upstream sources, which are not necessarily the latest official releases in the Metrology for the Factory of the Future project.
Those latest official releases of the project partners' code you find in the correspondingly named subfolders of this GitHub repository.
This software contains code for the firmware of the micro-controller board, which we call the "SmartUp Unit". The software is available in a GitHub repository. An installation guideline can be found in the corresponding wiki. The software is created with SW4STM32 (gcc). The underlying concept and its implementation has been published on Zenodo.
This repository contains the software needed to receive the measurement data from the Met4FoF SmartUp Unit. In this way, it can be viewed as a Python driver to make the measurement data available to other software like agentMET4FOF, as demonstrated in another repository. An introduction can be found on GitHub here.
This software package contains software tools that can be used to analyze measurement data which contain redundancy. The main part of the development has been performed at VSL (Netherlands), with help of the partner institutes NPL (UK), PTB (Germany) and UCAM (UK). The software is provided as a GitHub repository. The methods will are integrated into agentMET4FOF for a future release soon.
The main documentation can be found at ReadTheDocs.
This software implements an algorithm to reduce timing and noise effects in the data recorded by sensors in industrial sensor networks. A Bayesian approach is used to estimate parameters describing the levels of jitter and noise in the measured signal and parameters of a model for the underlying ‘true’ signal, which are used to provide estimates of the values of the true signal.
The software, developed in Python 3.8, is provided in the form of py-files in a GitHub repository. It is intended to be used within the multi-agent framework for the FoF project and thus is integrated into agentMET4FOF since its release v0.12.0.
For further details about the algorithm, please refer to the paper.
With the provided code we showcase an agent-based machine learning approach for online anomaly detection of (in our case simulated) sensor readings. The software is provided in a GitHub repository.
This is an implementation of an agent-based approach to interconnect hardware sensors from the manufacturer Seneca and processing the produced data streams including sensor data buffering as part of the agents' implementation. The software is provided as a GitHub repository.
The goal of this package is to provide a starting point for users in metrology and related areas who deal with time-dependent i.e., dynamic, measurements. The initial version of this software was developed as part of a joint research project of the national metrology institutes from Germany and the UK, i.e. Physikalisch-Technische Bundesanstalt and the National Physical Laboratory.
The documentation for PyDynamic can be found on ReadTheDocs.
The software can be found on GitHub.
Machine Learning tutorials oriented at begginers in data science. Methods are applied on Strathcylde's testbed data (Advanced Forming Research Centre | University of Strathclyde).
The Jupyter Notebooks are available on GitHub.
Machine Learning tutorials oriented at begginers in data science. Methods are applied on ZeMA's testbed data on Zenodo (Zentrum für Mechatronik und Automatisierungstechnik gGmbH).
The Jupyter Notebooks are available on GitHub.
time-series-metadata is a Python implementation of a metadata scheme for time-series with measurement uncertainties. It is developed jointly by software developers and researchers from Physikalisch-Technische Bundesanstalt (Germany) and Institute for Manufacturing (UK) as part of the joint European Research Project EMPIR 17IND12 Met4FoF and the German research project FAMOUS.
time-series-metadata is written in Python 3 and strives to run with all Python versions with upstream support. Currently it is tested to work with Python 3.5 to 3.8.
We provide access to the code hrough a GitHub repository.