This paper presents the application of the Extended Phase Space Topology (EPST) method in model-based diagnostics of nonlinear systems. A detailed nonlinear mathematical model of a servo electro-hydraulic system has been used to demonstrate the procedure. Two faults have been considered associated with the servo valve including the increased friction between spool and sleeve and the degradation of the permanent magnet of the valve armature. The faults have been simulated in the system by the variation of the corresponding parameters in the model and the effect of these faults on the output flow response has been investigated. A regression-based artificial neural network has been developed and trained using the EPST extracted features to estimate the original values of the faulty parameters and to identify the severity of the faults in the system.
How to Cite
Servo valves, Parameter Estimation, Neural Network, Model-based Diagnostics
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.