Online fault detection system for proportional hydraulic valves

Online fault detection system for proportional hydraulic valves This paper presents the development of a method for condition monitoring and online fault detection on proportional directional valves. The systems that such valves are part of might be sensitive for unexpected maintenance or long duration stops. Consequently, the implementation of a fault detection and monitoring system can reduce maintenance costs and increase safety. The method is based on monitoring both the valve supply current and spool position related to the spool positioning control signal. Therefore, it is applicable for valves with embedded electronics including spool position measurement and internal controller. The supply current and spool position behavior depends on the friction, flow forces, solenoid current, and valve closed loop controller performance. Furthermore, valve static and dynamic characteristics are influenced by the spool size, overlapping and manufacturing tolerances. The effectiveness of the method to monitor and detect faults in valves with different sizes and constructive parameters is shown experimentally using five different proportional valves. The proposed method requires reference parameters characterizing the valve operation without faults. Standard tests are proposed to determine healthy valve parameters. For the method valuation and validation, experimental results with the valve operating under healthy conditions and with induced faults were compared. Faults were added in a way to represent spool locking and increase of friction forces between the spool and sleeve. The obtained results show the capability the method for the detection of faults classified as severe even if the valve controller attempts to compensate the faulty behavior. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of the Brazilian Society of Mechanical Sciences and Engineering Springer Journals
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Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2018 by The Brazilian Society of Mechanical Sciences and Engineering
Subject
Engineering; Mechanical Engineering
ISSN
1678-5878
eISSN
1806-3691
D.O.I.
10.1007/s40430-018-1240-3
Publisher site
See Article on Publisher Site

Abstract

This paper presents the development of a method for condition monitoring and online fault detection on proportional directional valves. The systems that such valves are part of might be sensitive for unexpected maintenance or long duration stops. Consequently, the implementation of a fault detection and monitoring system can reduce maintenance costs and increase safety. The method is based on monitoring both the valve supply current and spool position related to the spool positioning control signal. Therefore, it is applicable for valves with embedded electronics including spool position measurement and internal controller. The supply current and spool position behavior depends on the friction, flow forces, solenoid current, and valve closed loop controller performance. Furthermore, valve static and dynamic characteristics are influenced by the spool size, overlapping and manufacturing tolerances. The effectiveness of the method to monitor and detect faults in valves with different sizes and constructive parameters is shown experimentally using five different proportional valves. The proposed method requires reference parameters characterizing the valve operation without faults. Standard tests are proposed to determine healthy valve parameters. For the method valuation and validation, experimental results with the valve operating under healthy conditions and with induced faults were compared. Faults were added in a way to represent spool locking and increase of friction forces between the spool and sleeve. The obtained results show the capability the method for the detection of faults classified as severe even if the valve controller attempts to compensate the faulty behavior.

Journal

Journal of the Brazilian Society of Mechanical Sciences and EngineeringSpringer Journals

Published: Jun 5, 2018

References

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