Data-Driven Virtual Flow Rate Sensor Development for Leakage Monitoring at the Cradle Bearing in an Axial Piston Pump

Minxing Liu, Garyeong Kim, Kai Bauckhage and Marcus Geimer ()
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Minxing Liu: Institute of Mobile Machines, Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany
Garyeong Kim: Institute of Mobile Machines, Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany
Kai Bauckhage: Bosch Rexroth AG, 72160 Horb am Necker, Germany
Marcus Geimer: Institute of Mobile Machines, Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany

Energies, 2022, vol. 15, issue 17, 1-18

Abstract: The leakage of the tribological contact in axial piston pumps significantly impacts the pump efficiency. Leakage observations can be used to optimize the pump design and monitor the behavior of the tribological contact. However, due to assembly limitations, it is not always feasible to observe the leakage of each tribological contact individually with a flow rate sensor. This work developed a data-driven virtual flow rate sensor for monitoring the leakage of cradle bearings in axial piston pumps under different operating conditions and recess pressures. The performance of neural network, support vector regression, and Gaussian regression methods for developing the virtual flow rate sensor was systematically investigated. In addition, the effect of the number of datasets and label distribution on the performance of the virtual flow sensor were systematically studied. The findings are verified using a data-driven virtual flow rate sensor to observe the leakage. In addition, they show that the distribution of labels significantly impacts the model’s performance when using support vector regression and Gaussian regression. Neural network is relatively robust to the distribution of labeled data. Moreover, the datasets also influence model performance but are not as significant as the label distribution.

Keywords: virtual sensor; leakage monitoring; data-driven method; machine learning; axial piston pump (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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