Demonstrating a Condition Monitoring Process for Axial Piston Pumps with Damaged Valve Plates

Authors

  • Nathan Keller Maha Fluid Power Research Center, Purdue University, 1500 Kepner dr., Lafayette, IN 47905, USA https://orcid.org/0000-0002-1762-5176
  • Annalisa Sciancalepore Maha Fluid Power Research Center, Purdue University, 1500 Kepner dr., Lafayette, IN 47905, USA https://orcid.org/0000-0001-7036-0149
  • Andrea Vacca Maha Fluid Power Research Center, Purdue University, 1500 Kepner dr., Lafayette, IN 47905, USA

DOI:

https://doi.org/10.13052/ijfp1439-9776.2324

Keywords:

Axial Piston Pump, Machine Learning, Condition Monitoring, Mobile Hydraulics, Fault Detection

Abstract

Unexpected pump failures in mobile fluid power systems result in monetary and productivity losses, but these failures can be alleviated by implementing a condition monitoring system. This research aims to find the best condition monitoring (CM) technique for a pump with the fewest number of sensors, to accurately detect a defective condition. The sensors choice in a CM system is a critical decision, and a high number of sensors may result in disadvantages besides additional cost, such as overfitting the CM model and increased maintenance.

A variable displacement axial piston pump is used as a reference machine for testing the CM technique. Several valve plates with various magnitudes of quantifiable wear and damage are used to compare “healthy” and “unhealthy” hydraulic pumps. The pump parameters are measured on a stationary test rig. This involves observing and detecting differences in pump performance between the healthy and unhealthy conditions and reducing the number of sensors required to monitor a pump’s condition. Observable differences in drain flow were shown, and machine learning algorithms were able to successfully classify a faulty and healthy pump with accuracies nearing 100%. The number of sensors was reduced by implementing a feature selection process and resulted in only five of the 23 sensors to correctly detect pump failure. These sensors measure outlet pressure, inlet pressure, drain pressure, pump speed, and pump displacement. The resulting reduction of sensors is reasonably affordable and relatively easy to implement on mobile applications.

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Author Biographies

Nathan Keller, Maha Fluid Power Research Center, Purdue University, 1500 Kepner dr., Lafayette, IN 47905, USA

Nathan Keller earned his Ph.D. and MS from Purdue University in 2020 at the Maha Fluid Power Research Center. Dr. Keller’s doctoral dissertation focused on demonstrating condition monitoring systems for axial piston pumps for mobile applications. His master’s thesis focused on developing energy saving hydraulic system architectures for the next generation of combine harvesters utilizing Displacement Control (DC) actuation. Dr. Keller has been instrumental in efficient next generation hydraulic designs for agricultural and construction equipment. He was also involved in the realization of the world’s first hydraulic hybrid SUV developed at the Maha Fluid Power Research Center. Dr. Keller is currently employed at Applied Fluid Power as a Systems Product Manager where he assists customers in designing efficient and effective hydraulic system solutions for both industrial and mobile applications.

Annalisa Sciancalepore, Maha Fluid Power Research Center, Purdue University, 1500 Kepner dr., Lafayette, IN 47905, USA

Annalisa Sciancalepore has been a Ph.D. student in Agricultural and Biological Engineering at Purdue University, IN, the US, since 2018. She received her B.Sc. in Mechanical engineering in 2015 and her M.Sc. in Mechatronic engineer in 2017 from the Polytechnic University of Turin, Italy. Annalisa’s research focuses on hydraulic systems optimization for material-handling machines. Specifically, she is investigating novel hydraulic circuits that use counterbalance valves with an adjustable pilot for energy-saving applications. Moreover, by combining her knowledge of hydraulic with her mechatronic background, she is developing novel numerical and experimental methods for condition monitoring of hydraulic systems and components.

Andrea Vacca, Maha Fluid Power Research Center, Purdue University, 1500 Kepner dr., Lafayette, IN 47905, USA

Andrea Vacca holds the Maha Fluid Power faculty chair position at Purdue University, and leads the Maha Fluid Power Research Center of the same University. Dr. Vacca earned his Ph.D. from the University of Florence (Italy) in 2005. Before joining Purdue University in 2010, Dr. Vacca was Assistant Professor of Fluid Machinery at the University of Parma (Italy). Fluid power technology has been Dr. Vacca’s major research interest since 2002. His research team has developed original numerical and experimental techniques for hydraulic systems and components. The team as also formulated several novel solutions, including new concepts to perform hydraulic actuations as well as new designs for pumps and motors. Dr. Vacca is the author of more than 150 papers, most of them published in international journals or conferences. Dr. Vacca is also recipient of the 2019 Bramah medal of the Institution of Mechanical Engineers (IMechE). He is the chair of Fluid Power Systems and Technology Division (FPST) of ASME, and a former chair of the SAE Fluid Power division. Dr. Vacca is also Treasurer and Secretary of the Board of the Global Fluid Power Society (GFPS). Furthermore, he is also Editor in Chief of the International Journal of Fluid Power and he is in the Editorial Board of Actuators, the Journal of Dynamics and Vibroacoustics and the Journal of Hydromechatronic.

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Published

2022-02-22

How to Cite

Keller, N. ., Sciancalepore, A. ., & Vacca, A. . (2022). Demonstrating a Condition Monitoring Process for Axial Piston Pumps with Damaged Valve Plates. International Journal of Fluid Power, 23(02), 205–236. https://doi.org/10.13052/ijfp1439-9776.2324

Issue

2022: Vol 23 Iss 2

Section

Original Article

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