Industry 4.0 enables the modernisation of machines and opens up the digitalisation of processes in the manufacturing industry. As a result, these machines are ready for predictive maintenance as part of Industry 4.0 services. The benefit of predictive maintenance is that it can significantly extend the life of machines. The integration of predictive maintenance into existing production environments faces challenges in terms of data understanding and data preparation for machines and legacy systems. Current AI frameworks lack adequate support for the ongoing task of data integration. In this context, adequate support means that the data analyst does not need to know the technical background of the pilot’s data sources in terms of data formats and schemas. It should be possible to perform data analyses without knowing the characteristics of the pilot’s specific data sources. The aim is to achieve a seamless integration of data as information for predictive maintenance. For this purpose, the developed data-sharing infrastructure enables automatic data acquisition and data integration for AI frameworks using interoperability methods. The evaluation, based on two pilot projects, shows that the step of data understanding and data preparation for predictive maintenance is simplified and that the solution is applicable for new pilot projects.

Heterogeneous test processes with respect to test script languages are an integral part of the development process of mechatronic systems that are carried out in supply chains. Up to now, test cases are not exchangeable between test processes because interoperability is not given. The developed approach enables the source-to-source compiling of test cases between test script languages. With this, the interoperability of test cases is achieved, and seamless integration within the supply chain is possible. The developed approach uses transcompilers as a baseline. In doing so, an interoperability model for test cases is presented. Based on the interoperability model, a source-to-source compiling for test cases is shown. The outcome is a prototype that handles test script languages, which are different with respect to type safety and applied programming paradigms. The approach ensures that test cases are still understandable and usable for test reports. The evaluation confirms the translation capabilities as well as the readability of the generated test case for the high-lift scenario from aviation. The interoperability of test cases within the supply chain enables the formalisation of procedural test knowledge to be used in a broad range of future scenarios, such as test automation, digital twins and predictive maintenance.

A Semantic Mediator was conceived in the CRC 637 (The Collaborative Research Centre 637 “Autonomous Cooperating Logistic Processes” focused on adaptive logistic processes including autonomous capabilities for the decentralised coordination of autonomous logistic objects in a heterarchical structure.) to tackle problems of interoperability of heterogeneous information sources in autonomous cooperating logistics processes. Since the conclusion of the CRC 637, the Semantic Mediator has been developed further and successfully transferred to interoperability problems in different domains, including Industry 4.0, the Internet of Things, and Product Lifecycle Management. This paper will introduce the Semantic Mediator and present examples of its successful application.