Research project: Prognostics

Project director: Prof. Dr.-Ing. Peter Zeiler

Project worker: Fabian Mauthe

Brief description of the research project:

The research project aims to enable users, especially SMEs, to realise the latest maintenance strategies, primarily predictive maintenance. To this end, it will develop a method of assessment and selection which takes account of application-specific and industry-related framework conditions. This categorisation will form the basis for the generation of sets of real degradation data, which will then be made freely available. This helps users with suitable projects to select suitable methods and also to assess the implementability and the risks. The method employed here can be data driven, e.g. an artificial neuronal network or Support Vector Machines. The prognostic method can also be model-based and use a physical model of the degradation process, e.g. a crack growth equation or a wear model. The possibilities described are illustrated in Figure 3.

Most of the PHM research being undertaken at present relates not to selection and assessment but to the development or further improvement of prognostic methods or their exemplary application to an individual problem. It is essential that users in particular have a methodology available to assess and select suitable methods as they start to implement predictive maintenance. The research project aims to close the gaps in the current state of the research and the state of the art, and thus play a significant role in making predictive maintenance more widespread. The actual innovation consists particularly in linking up basic research results obtained during the development of prognostic methods with the actual practical demands of industry. Esslingen University of Applied Sciences is working with three different-sized industrial partners on this research project. The project is funded by the Baden-Württemberg Ministry for Science, Research and the Arts.