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15:30 ics Saal 4
Data-based condition monitoring of a fluid power system with varying oil parameters
Nikolai Helwig, Centre for Mechatronics and Automation Technology (ZeMA), Germany
Andreas Schütze, Lab for Measurement Technology, Saarland University
In this work, an automated statistical approach for the condition monitoring of a fluid
power system based on a process sensor network is presented. In a multistep process,
raw sensor data are processed by feature extraction, selection and dimensional
reduction and finally mapped to discriminant functions which allow the detection and
quantification of fault conditions. Experimentally obtained training data are used to
evaluate the impact of temperature and different aeration levels of the hydraulic fluid on
the detection of pump leakage and a degraded directional valve switching behavior.
Furthermore, a robust detection of the loading state of the installed filter element and
an estimation of the particle contamination level is proposed based on the same