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.
A neural based approach and probability density approximation for fault detection and isolation in nonlinear systems
MONTISCI, AUGUSTO
2011-01-01
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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