A Neural Based Approach and Probability Density Approximation for Fault Detection and Isolation in Nonlinear Systems

Abstract : A locally recurrent neural network based fault detection and isolation approach is presented. A model of the system under test is created by means of a dynamic neural network. The fault detection is performed on the basis of the statistical analysis of the residual provided by the estimated density shaping of residuals in the case of nominal value of all the parameters, made of a simply neural network. The approach is illustrated by using the Rössler hyperchaotic system.
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Lazaros Iliadis; Chrisina Jayne. 12th Engineering Applications of Neural Networks (EANN 2011) and 7th Artificial Intelligence Applications and Innovations (AIAI), Sep 2011, Corfu, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-363 (Part I), pp.296-305, 2011, Engineering Applications of Neural Networks. 〈10.1007/978-3-642-23957-1_34〉
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P. Boi, A. Montisci. A Neural Based Approach and Probability Density Approximation for Fault Detection and Isolation in Nonlinear Systems. Lazaros Iliadis; Chrisina Jayne. 12th Engineering Applications of Neural Networks (EANN 2011) and 7th Artificial Intelligence Applications and Innovations (AIAI), Sep 2011, Corfu, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-363 (Part I), pp.296-305, 2011, Engineering Applications of Neural Networks. 〈10.1007/978-3-642-23957-1_34〉. 〈hal-01571357〉

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