Economic Fault Detection for Axial Piston Pumps Under Variable Operating Conditions

Authors

  • Svenja Horn Institute of Mechatronic Engineering, Technische Universität Dresden, Germany
  • Simon Knoll Institute of Mechatronic Engineering, Technische Universität Dresden, Germany
  • Ahmed Shorbagy Institute of Mechatronic Engineering, Technische Universität Dresden, Germany
  • Michael Lenz Institute of Mechatronic Engineering, Technische Universität Dresden, Germany
  • Jürgen Weber Institute of Mechatronic Engineering, Technische Universität Dresden, Germany

DOI:

https://doi.org/10.13052/ijfp1439-9776.2628

Keywords:

Economic fault detection, condition monitoring, machine learning, predictive maintenance, transferability, remaining lifetime

Abstract

This paper presents a method for an economic condition monitoring implementation on axial piston pumps. The vision is to use only one “low-budget” sensor that records as much information as possible. It shows the detection of three typical faults – valve plate cavitation, slipper wear and axial slipper-piston clearance. A variety of sensors were used to collect data on efficiencies, flow rates, temperatures, and accelerations of the new and the faulty pump for 100 different operating conditions. Using a representative set of operating conditions with different speed, pressure and swash plate angle, the faults were predicted for two different machine learning algorithms for different sensors. The triaxial acceleration sensor achieves the best accuracy of 100% with a sampling rate of 16.4 kHz. To further reduce costs, the sampling rate is virtually downsampled until 4.1 kHz, resulting in a drop to 91% prediction accuracy. Moreover, the prediction accuracy for different sets of operating conditions with constant speed, pressure and swash plate angle are compared to each other showing best results for remaining the speed constant.

This paper shows, that it is possible to achieve accuracies over 98% on fault detection with only using the y-component of an acceleration sensor with a sampling rate of 4.1 kHz.

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Author Biographies

Svenja Horn, Institute of Mechatronic Engineering, Technische Universität Dresden, Germany

Svenja Horn received her diploma degree in Aircraft Construction from the Technical University of Dresden in 2018. Since then, she has been working as a research assistant at the Chair of Fluid-Mechatronic Systems, focusing on improving the efficiency and service life of axial piston pumps. Her research concentrates on tribological phenomena at the slipper-swashplate interface, which she investigates using simulations, a hydrostatic tribometer and pump measurements. Furthermore, she applies machine learning algorithms for pump condition monitoring and lifetime prediction.

Simon Knoll, Institute of Mechatronic Engineering, Technische Universität Dresden, Germany

Simon Knoll graduated from the Technical University of Dresden in 2025 with a diploma degree in Mechanical Engineering, specializing in fluid power. During his studies, he focused on research related to the slipper-swashplate interface in axial piston pumps and using machine learning with sensor data to monitor pump conditions. He is currently working as a research assistant at the Chair of Fluid-Mechatronic Systems.

Ahmed Shorbagy, Institute of Mechatronic Engineering, Technische Universität Dresden, Germany

Ahmed Shorbagy obtained his Master’s degree in Aerospace Engineering from the Technical University of Berlin in 2015. Since completing his degree, he has been engaged in the development and testing of Computational Fluid Dynamics and Fluid-Structure Interaction models to enhance engineers’ understanding of positive displacement machines. In his role as a research assistant at the Chair of Fluid-Mechatronic Systems, he integrated experimental methods with computational approaches to facilitate a thorough analysis of tribological interfaces, linking their behavior under various operational conditions to the overall efficiency of the pumps. His work aims to clarify the mechanisms of wear and friction, contributing to improved design and operational strategies for hydraulic components.

Michael Lenz, Institute of Mechatronic Engineering, Technische Universität Dresden, Germany

Michael Lenz graduated from TU Dresden in Applied Mechanics in 2014. As a research assistant first at TU Dresden’s Chair of Dynamics and Mechanism Design and more recently the Chair of Fluid-Mechatronic systems, he has worked on a variety of subjects such as resilient rail wheels and rubber mechanics, ring spinning, sealing wear and hydropump acoustics. Current research interests include pump dynamics and psychoacoustics as well as special sealing applications.

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Published

2025-07-13

How to Cite

Horn, S. ., Knoll, S. ., Shorbagy, A. ., Lenz, M. ., & Weber, J. . (2025). Economic Fault Detection for Axial Piston Pumps Under Variable Operating Conditions. International Journal of Fluid Power, 26(02), 319–342. https://doi.org/10.13052/ijfp1439-9776.2628

Issue

2025: Vol 26 Iss 2

Section

Maha Fluid Power 2024

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