MONITORING THE CONDITION OF A VALVE AND LINEAR ACTUATOR IN HYDRAULIC SYSTEMS

Authors

  • Jahmy J. Hindman John Deere Construction & Forestry Division
  • Richard Burton University of Saskatchewan, Mechanical Engineering Department
  • Greg Schoenau University of Saskatchewan, Mechanical Engineering Department

Keywords:

condition monitoring, neural network, valve, cylinder, actuator

Abstract

The topic of condition monitoring has been a growing area of research in both academia and industry for much of the last two decades. Condition monitoring of fluid power equipment has been no exception to this trend. Much of the research work associated with monitoring the condition of fluid power equipment has centered on pump and motor components due to their relatively high cost and complexity. The work in this paper focuses on the lesser expensive, but more common components of valves and linear actuators. The primary focus of the work presented here pertains to assessing the independent component condition of a valve-controlled linear actuator circuit. The paper first presents simulation studies to establish techniques for proper data collection, neural network training and output interpretation. The neural network approach is then applied to a valve and linear actuator of a John Deere 410E Backhoe Loader. The results indicate that the concept can be applied to a commercial system and is feasible for implementation.

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Author Biographies

Jahmy J. Hindman, John Deere Construction & Forestry Division

Jahmy Hindman Jahmy Hindman obtained his B.S. degree in mechanical engineering from Iowa State University in 1998. He obtained his M.Sc. degree in mechanical engineering from the University of Saskatchewan in 2002 where he is currently a PhD candidate. He is also the Engineering Supervisor of Hydraulics and Electrical groups for the John Deere Four-Wheel-Drive Loader group. His interests lie in hydraulic system design and control and artificial intelligence.

Richard Burton, University of Saskatchewan, Mechanical Engineering Department

Richard Burton Richard Burton received his PhD, and MSc degrees in Mechanical Engineering from the University of Saskatchewan. He is a professor of Mechanical Engineering at the University of Saskatchewan, has professional engineering status (P.Eng) with the Association of Professional Engineers of Saskatchewan and is a Fellow of ASME. Burton is involved in research pertaining to the application of intelligent theories to control and monitoring of hydraulics systems, component design, and system analysis.

Greg Schoenau, University of Saskatchewan, Mechanical Engineering Department

Greg Schoenau Professor of Mechanical Engineering at the University of Saskatchewan. He was head of that Department from 1993 to 1999 and is now Associate Dean of Research. (2006). He obtained B.Sc. and M. Sc. Degrees from the University of Saskatchewan in mechanical engineering in 1967 and 1969, respectively. In 1974 he obtained his Ph.D. from the University of New Hampshire in fluid power control systems. He continues to be active in research in this area and in the thermal systems area as well. He has also held positions in numerous outside engineering and technical organizations.

References

Andrews J. and Henry J. 1997. A computerised fault

tree construction methodology. Proc IMechE, Part

E, Journal of Process Mechanical Engineering. 211,

-183.

Cao, H. 2001. Parameter Estimation Using Extended

Kalman Filter for the Swash Plate Assembly and

the Control Piston in a Load Sensing Pump.

M.Sc.Thesis. University of Saskatchewan.

Crowther, W., Edge, K., Burrows, C., Atkinson, R.

and Woollons, D. 1998. Fault diagnosis of a hydraulic

actuator circuit using neural networks-an

output vector space classification approach. Proc

Instn Mech Engrs Vol. 212, Part I. pp. 57-69.

Darling R. and Tilley D. G. 1993. Progress towards a

general purpose technique for the condition monitoring

of fluid power systems. Proc IMechE Conference

on Aerospace Hydraulics and Systems.

London, 47-55.

Hagan, M. T. and Menhaj, M. 1994. Training Feed

Forward Networks with the Marquardt Algorithm.

IEEE Transactions on Neural Networks. Vol. 5(6).

pp. 989-993.

Hindman, J. 2001. Condition Monitoring of Valves

and Actuators in a Mobile Hydraulic System Using

ANN and Expert Data. M.Sc. Thesis. University of

Saskatchewan.

Hindman, J., Burton, R., and Schoenau, G. 2002. Condition

Monitoring of Fluid Power Systems: A Survey.

SAE Journal of Commercial Vehicles, pp. 69-75.

Le, T., Watton, J. and Pham, D. 1997. An artificial

neural network based approach to fault diagnosis

and classification of fluid power systems. Proc Instn

Mech Engrs Vol. 211, Part I. pp. 307-317.

Liang, A. and Sepheri, N. 2005. Hydraulic Actuator

Leakage Fault Detection using Extended Kalman

Filter, International Journal of Fluid Power,- FPNI,

Vol. 6, Number 1, pp 41-52.

Rosa, A. 2001. Estimating Parameters of a Proportional

Solenoid Valve using Neural Neworks. M.Sc. Thesis.

University of Saskatchewan.

Watton J. and Stewart J. C. 1996. Co-operating expert

knowledge and artificial neural networks for

fault diagnosis of electrohydraulic cylinder position

control systems. Proc 3rd JHPS International Symposium

on Fluid Power. Yokohama. 217-222.

Watton J., Lucca-Negro O. and Stewart J. C. 1994.

An on-line approach to fault diagnosis of fluid

power cylinder drives systems. Proc IMechE Journal

Systems and Control Engineering. Vol 208,

-262.

Wright, G. 2001. Parameter Estimation of a Hydraulic

Proportional Valve Using Extended Kalman Filtering.

M.Sc. Thesis. University of Saskatchewan.

Yunbo, H., Lim, G., Chua, P. and Tan, A. 2001.

Monitoring the Condition of Loaded Modern Water

Hydraulic Axial Piston Motor and Cylinder. Proceedings

of the Fifth International Conference on

Fluid Power Transmission and Control, pp. 447-

Hangzhou, China.

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Published

2006-03-01

How to Cite

Hindman, J. J., Burton, R., & Schoenau, G. (2006). MONITORING THE CONDITION OF A VALVE AND LINEAR ACTUATOR IN HYDRAULIC SYSTEMS. International Journal of Fluid Power, 7(1), 15–25. Retrieved from https://journals.riverpublishers.com/index.php/IJFP/article/view/559

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Original Article