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Headerbild Deltra Pro


Ongoing project. Increasing product and process quality by analysing signal parameters in broadband networks for SMEs in real time.



Efficiency and quality are decisive factors for the success of a manufacturing company. Deviations from optimised TARGET processes lead to reduced product quality or inefficient processes due to increased effort or longer production times. Actual processes are usually recorded manually or with the help of a large number of sensors to determine parameters such as temperature, distance or noise. This is time-consuming, error-prone and only possible on a random basis. Machine data is often not sufficiently available for automated evaluation in SMEs. The use of sensor technology is often not economical due to the large number of sensors required. This is further exacerbated by manufacturing conditions typical of SMEs, such as small batch sizes, a high number of product variants or workshop-like, partially manual production.



The aim of the KMU-innovativ project DeltaPro is to develop a monitoring system for production processes under typical SME manufacturing conditions. By using broadband networks available on site (e.g. mobile communications, WLAN), recording signal changes and analysing them using artificial intelligence (AI), production fluctuations are detected in real time. The result is a solution that can be retrofitted efficiently and easily, even in smaller production facilities across all industries.

Efficiency and quality are decisive factors for the success of manufacturing companies. Actual processes are often recorded manually or using a large number of sensors. Machine data for automated evaluation in SMEs is often not sufficiently available.

In the DeltaPro project, the partners pool their expertise and apply it in their own demonstrator with the support of the application and associated partners. 


Recording the signal changes of the wide fire nets in trailer production. Source: STEMA Metallleichtbau GmbH.

Challenge for ASTRUM IT

ASTRUM IT is the project coordinator and is responsible for overall project management. ASTRUM IT is focussing on the design and implementation of the software framework for predicting production processes. This application creates the framework for the innovative sensor developed by the partners and fully integrates it for use in the operational environment. ASTRUM IT is also contributing its machine learning expertise to the development of the prediction model.

In its own sub-project, the first step is to define the requirements using the user-centred design method. To this end, ASTRUM IT conducts interviews and surveys, creates persona profiles, for example, and describes the processes using BPMN (Business Process Model and Notation). The system architecture is then defined on this basis and the interfaces specified. ASTRUM IT works with the partners on the system concept and the overall system architecture to define the basic architecture of the software used in this project for the innovative broadband sensor developed by the partners.

The prediction model for the production environment is then developed together with the partners. ASTRUM IT researches possible analytical processes based on the requirements and evaluates initial process candidates based on the data collected.

The novel broadband sensor is then integrated into a prediction framework for an operational early warning system. For this purpose, the system architecture and the novel sensor are connected via the software interfaces.

In the final step, the developed system is integrated into the operational environment, evaluated and adapted to the knowledge gained. System optimisation also takes place here. 


This research and development project is funded by the Federal Ministry of Education and Research (BMBF) as part of the ‘KMU-innovativ: Produktionsforschung’ (02K23K000) funding programme and supervised by the Project Management Agency Karlsruhe (PTKA). Responsibility for the content of this publication lies with the author.

Bundesministerium für Bildung und Forschung