This paper presents the application of recurrence plots (RPs) and recurrence quantification analysis (RQA) 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 RPs and RQA parameters has been investigated. A regression-based artificial neural network has been finally developed and trained using the RQA parameters to estimate the original values of the faulty parameters and identify the severity of the faults in the system.
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