PREDICT

Significant advances in sensor technology and data analysis now provide this research with precise insights into the operating conditions of aircraft engines. These insights enable the early detection of failures and extend service life through targeted maintenance of the systems. Using these methods promotes the efficiency and reliability of the systems. Integrating these methods into life cycle models improves the accuracy of long-term reliability and maintenance requirement forecasts. Maintenance strategies optimised using sound diagnostic and prognostic methods can significantly reduce operating costs while increasing reliability.

Contribution Institute for AI Safety and Security

The Institute for AI Safety and Security is participating in this project by developing methods to ensure the data to be processed is of a high quality. This includes creating customised data quality assessment models and developing concepts for data quality management and standardisation — including certification — as well as creating prototype services for the developed concepts.

Our Institute also links distributed data rooms with each other because not all data and models will be bundled centrally for industrial exploitation, for example in predictive maintenance. Connecting data sources and services to Gaia-X-compatible connectors is important in this context, as it ensures trustworthy and traceable data and service exchange with industry partners. Traceability with regard to regulatory requirements is also important.

Further information can be found on the official PREDICT project page.