Diagnosis of nonlinear systems using kernel machines
نویسندگان
چکیده
The diagnosis of a system lies on fault detection and localization, but also on magnitudes estimation of the detected faults. A method widely used for this diagnosis is Principal Component Analysis (PCA). After detection, the faults are frequently localized using the technique of structuring residues. It is to find a modification that makes the transformed residues sensitive or insensitive to certain faults. The amplitude of the detected and localized fault is then estimated using a variables reconstruction technique. The latter relies on estimating a variable from the PCA model and other available variables [1, 2].
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