– Digital twin (simulation system based on functional mock-up units)
– Decentralised testing (universal infrastructure with high data security)
– Predictive maintenance (adaptive and self-learning AI platform)
(PresseBox) (eXept, 04.07.2024)
Companies can use the BSFZ seal to demonstrate their innovative expertise to the outside world. The seal is awarded exclusively by the Certification Centre for Research Grants (BSFZ, Bescheinigungsstelle Forschungszulage) to companies for the promotion of research and development (R&D) projects.
Due to the novelty of the three R&D projects, eXept was awarded the BSFZ seal by the Research Grant Certification Centre in June 2024. The three R&D projects are as follows:
- Digital twin: Development of a simulation system for modelling production processes without real prototypes.
- Decentralised testing: Development of a universally applicable infrastructure for carrying out functional tests with 100% time data and high system reliability.
- Predictive maintenance: Development of an adaptive AI platform for condition-based maintenance and service life prediction.
The results of the research projects are incorporated into the AIDYMO and expecco automation solutions.
Digital twin:
eXept already has extensive experience in the test automation of digital twins and the development of software for “virtual commissioning” and “simulation-based engineering”, particularly through its partnership with ISG.
What is new about this project is the development of a simulation system based on functional mock-up units (FMUs). A modular NoCode system enables the complete virtual integration and mapping of complex production systems without physical prototypes.
Decentralised testing:
Current functional tests, e.g. for cars or electrical appliances, often do not offer real-time control from remote locations with insufficient data security.
The aim is to develop a hybrid autonomous test infrastructure with dedicated API gateways. The transmission of 100% time data should fulfil high system security requirements.
eXept will contribute its expertise in the development of encryption, digital signatures and content negotiation to the project.
Predictive maintenance:
Cost reduction and reliable forecasts for systems are a research and development driver for every industrial operation. eXept already has extensive experience in the automation of processes and can now expand its knowledge portfolio.
Adaptive, self-learning predictive maintenance systems can be used for the first time to dynamically analyse machine data and predict failures and product quality. This leads to a reduction in downtimes and optimisation of maintenance resources.
The specific objectives and challenges of the three innovative R&D projects and the results achieved, including solutions to problems and closing knowledge gaps, are documented in detail by eXept.