Fault detection and diagnosis based on modeling and estimation methods

Authors:
Sunan Huang;Kok Kiong Tan
Affiliations:
Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore;Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
Venue:
IEEE Transactions on Neural Networks
Year:
2009

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Abstract

This paper investigates the problem of fault detection and diagnosis in a class of nonlinear systems with modeling uncertainties. A nonlinear observer is first designed for monitoring fault. Radial basis function (RBF) neural network is used in this observer to approximate the unknown nonlinear dynamics. When a fault occurs, another RBF is triggered to capture the nonlinear characteristics of the fault function. The fault model obtained by the second neural network (NN) can be used for identifying the failure mode by comparing it with any known failure modes. Finally, a simulation example is presented to illustrate the effectiveness of the proposed scheme.