Condition Monitoring of an Axial Piston Pump on a Mini Excavator
DOI:
https://doi.org/10.13052/ijfp1439-9776.2422Keywords:
Axial piston pump, machine learning, condition monitoring, Mobile Hydraulics, fault detectionAbstract
The focus 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 detecting valve plate faults were the drain pressure, pump port pressures, engine speed, and pump displacement. Fault detectability 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 detectability 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 detect pump faults.
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References
Silva Gabriel, “Wear Generation in Hydraulic Pump,” SAE Tech. Pap. Ser., no. 901679, p. 18, 1990, doi: 10.4271/901679.
F. Feicht, “Factors Influencing Service Life and Failure of Hydraulic Components,” Olhydraulik and Pnuematik, vol. 20, pp. 804–806, 1976.
J. Watton, Modelling, Monitoring and Diagnostic Techniques for Fluid Power Systems. London: Springer, 2007.
F. Lu, R. Burton, and G. Schoenau, “Feasibility study on the use of a neural network to detect and locate excess piston wear in an axial piston pump,” in Innovations in Fluid Power, 7th Bath International Fluid Power Workshop, 1994.
F. Lu, T. Burton, and G. Schoenau, “A Neural Network Based Incipient Fault Detection System for an Axial Piston Hydraulic Pump,” in Proceedings of 1995 IASTED International Conference, SIP-95, 1995.
T. Ramdén, P. Krus, and J. Palmberg, “Condition monitoring of fluid power pumps using vibration measurements and neural networks trained with complex optimisation,” in Vibration and Noise 95, 1995.
W. Crowther, K. A. Edge, C. R. Burrows, R. M. Atkinson, and D. J. Woollons, “Fault diagnosis of a hydraulic actuator circuit using neural networks - An output vector space classification approach,” Proc. Inst. Mech. Eng. Part I J. Syst. Control Eng., vol. 212, no. 1, pp. 57–68, 1998, doi: 10.1243/0959651981539299.
G. J. Schoenau, J. S. Stecki, and R. T. Burton, “Utilization of artificial neural networks in the control, identification and condition monitoring of hydraulic systems – An overview,” SAE Tech. Pap., vol. 109, no. 2000, pp. 205–212, 2000, doi: 10.4271/2000-01-2591.
Z. Li, “Condition Monitoring of Axial Piston Pump,” no. November, p. 134, 2005.
C. Lu, N. Ma, and S. Wang, “Fault detection for hydraulic pump based on chaotic parallel RBF network,” EURASIP J. Adv. Signal Process. 2011, no. 1, p. 49, 2011.
J. Du, S. Wang, and H. Zhang, “Layered clustering multi-fault diagnosis for hydraulic piston pump,” Mech. Syst. Signal Process., vol. 36, no. 2, pp. 487–504, 2013, doi: 10.1016/j.ymssp.2012.10.020.
Y. Lan et al., “Fault diagnosis on slipper abrasion of axial piston pump based on Extreme Learning Machine,” Meas. J. Int. Meas. Confed., vol. 124, no. October 2017, pp. 378–385, 2018, doi: 10.1016/j.measurement.2018.03.050.
Nathan Keller, Annalisa Sciancalepore, and Andrea Vacca. “Demonstrating a Condition Monitoring Process for Axial Piston Pumps with Damaged Valve Plates.” International Journal of Fluid Power (2022): 205–236.
M. Zecchi, “A novel fluid structure interaction and thermal model to predict the cylinder block/valve plate interface performance in swash plate type axial piston machines,” Purdue University, 2013.
A. Busquets, “An Investigation of Micro-Surface Shaping on the Piston/Cylinder Interface of Axial Piston Machines,” Purdue University, 2018.
D. Mizell, “A Study of the Piston Cylinder Interface of Axial Piston Machines,” Purdue University, 2018.
X. Wang, S. Lin, S. Wang, Z. He, and C. Zhang, “Remaining useful life prediction based on the Wiener process for an aviation axial piston pump,” Chinese J. Aeronaut., 2016, doi: 10.1016/j.cja.2015.12.020.
R. Rahmfeld and M. Ivantysynova, “Energy Saving Hydraulic Actuators for Mobile Machines,” in 1st Bratislav Fluid Power Symposium, 1998.
C. Williamson, “Power Management for Multi-Actuator Mobile Machines with Displacement Controlled Hydraulic Actuators,” Purdue University, 2010.
J. Zimmerman, “Toward Optimal Multi-Actuator Displacement Controlled Mobile Hydraulic Systems,” Purdue University, 2012.
E. Busquets, “Advanced Control Algorithms for Compact and Highly Efficient Displacement-Controlled Multi-Actuator and Hydraulic Hybrid Systems,” Purdue University, 2016.
N. S. Altman, “An introduction to kernel and nearest-neighbor nonparametric regression,” American Statistician, vol. 46, no. 3. pp. 175–185, 1992, doi: 10.1080/00031305.1992.10475879.
A. K. Jain, “Data clustering: 50 years beyond K-means,” Pattern Recognition Letters, vol. 31, no. 8. pp. 651–666, 2010, doi: 10.1016/j.patrec.2009.09.011.
