Condition Monitoring of an Axial Piston Pump on a Mini Excavator

Nathan Keller, Annalisa Sciancalepore, Andrea Vacca

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

The locus of this paper is to show the process of developing a condition monitoring system for an axial piston pump mounted on a mini excavator. This work outlines some previous condition monitoring work on axial piston pumps but addresses the lack of research conducted on mobile hydraulics. The valve plate of the pump is chosen as a case study to demonstrate varying degrees of wear and damage to represent healthy and faulty pump conditions. The wear and damage of these valve plates is measured using an optical profilometer, and efficiency measurements were conducted to characterize the fault levels. Once the faults were characterized, the mini excavator was introduced and instrumented to demonstrate what parameters were being considered. Next, three duty cycles were introduced: controlled, digging, and different operator cycles. The controlled cycles are a very repeatable condition that eliminated the need of an operator. The digging cycle was more of a realistic cycle where an operator dug into a loose pile of soil. The different operator cycle is the same as the digging cycle, but a different operator was employed. The sensors that proved to be the most useful in delecting valve plate faults were the drain pressure, pump port pressures, engine speed, and pump displacement. Fault deteclabilily accuracies of 100% were achievable under the controlled cycle utilizing the Fine KNN classification machine learning algorithm. The digging cycle could achieve a fault detection accuracy of 93.6% using the same algorithm and sensors. Finally, the cross-compatibility between a model trained under once cycle and using data from another cycle as an input was investigated. This study showed that a model trained under the controlled duty cycle does not give reliable and accurate fault delectability for data run in a digging cycle, below 60% accuracies. However, cross-compatibility may be achievable if more extreme faults are present. This work concluded by recommending a diagnostic function for mobile machines to perform a preprogrammed operation to reliably and accurately delect pump faults.

Original languageEnglish
Pages (from-to)171-206
Number of pages36
JournalInternational Journal of Fluid Power
Volume24
Issue number2
DOIs
StatePublished - 2023
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2023 River Publishers.

Keywords

  • Axial piston pump
  • condition monitoring
  • fault detection
  • machine learning
  • mobile hydraulics

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