Linear actuators, implemented as closed hydraulic systems, without external piping, are a state of the art drive concept, see (Gannon, 2017). Collecting data, used to train a condition monitoring (CM) for such drives, running 24/7, is cumbersome or even not possible. To gain training data, containing valid and invalid system states, we developed a simulation model, consisting of the most relevant physical effects. The simulated data are evaluated by a one-step feature approach and additionally with a two-step approach using two less complex fault state separation methods. In the end, the two-step method showed to be slightly better. The condition monitoring is not only used to recognize, but also to distinguish between accumulator and pump faults.
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Hydraulics, Linear Actuator, Fault Diagnosis, Digital Twin, Machine Learning
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