An approach to robust fault detection for nonlinear system based on RBF neural network observer (Q2725371)
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scientific article; zbMATH DE number 1619181
| Language | Label | Description | Also known as |
|---|---|---|---|
| English | An approach to robust fault detection for nonlinear system based on RBF neural network observer |
scientific article; zbMATH DE number 1619181 |
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20 March 2003
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radial basis function neural networks
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training algorithms
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robust fault detection and isolation
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affine nonlinear systems
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state estimation
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residuals
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classifier
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An approach to robust fault detection for nonlinear system based on RBF neural network observer (English)
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The paper presents a robust fault detection and isolation (FDI) method for affine nonlinear systems, which is based on the use of radial basis function (RBF) neural networks (NNs). The RBF NN is employed to approximate the nonlinear item of the monitored system in order to improve the accuracy of state estimation. It is shown that the resulting state estimation error tends to zero asymptotically, i.e., the method eliminates the effect of modeling errors on the residuals. In addition, an NN classifier is employed to complete the fault isolation. A new training (weight adjustment) algorithm is derived to enhance the robustness of FDI. A simulation example is included.
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