Abstract
Machine learning techniques are continuously gaining attention and importance in several technical domains. In the field of engineering, they can potentially provide manifold advantages for condition monitoring. However, availability of extensive operation data is a limiting factor. In this contribution, a simulation-based approach is presented, which allows an efficient generation of training data. Based on a lumped parameter simulation, a database of time-series data is generated for a hydraulic reference system. In order to incorporate states of faulty machine operation in the database, means to model component faults in the simulation are assessed. Further, a procedure for an automated training data generation is presented.
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