Abstract
In this paper, we develop a fault identification approach for electro-hydraulic servo actuators based on injecting a pre-defined diagnostic signal into the system and then extracting fault-related features from the phase space topology. Next, we build regression models using an artificial neural network, which maps the feature space to fault space to identify the faults represented by the system’s parameters. The performance of the proposed fault identification approach is evaluated when the degradation of permanent armature occurs. The effect of parametric faults on the dynamics is studied and discussed. The different excitation of the system is considered, and the robustness of the proposed method under the condition of noise is also explored. The obtained results indicate the effectiveness of injected diagnostic signals in enriching the dynamics of the system and increasing the quality of extracted features and the accuracy of trained artificial neural networks.
Published Version
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