Fault Diagnosis Using Wavelet Neural Networks
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International Journal of Systems Science - Advances in Sliding Mode Observation and Estimation (Part Two)
Fault detection and diagnosis based on modeling and estimation methods
IEEE Transactions on Neural Networks
A neural-fuzzy sliding mode observer for robust fault diagnosis
ACC'09 Proceedings of the 2009 conference on American Control Conference
IEEE Transactions on Neural Networks
Dealing with fault dynamics in nonlinear systems via double neural network units
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
A robust fault detection and isolation scheme for robot manipulators based on neural networks
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing
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Information Sciences: an International Journal
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The paper presents a robust fault diagnosis scheme for detecting and approximating state and output faults occurring in a class of nonlinear multiinput-multioutput dynamical systems. Changes in the system dynamics due to a fault are modeled as nonlinear functions of the control input and measured output variables. Both state and output faults can be modeled as slowly developing (incipient) or abrupt, with each component of the state/output fault vector being represented by a separate time profile. The robust fault diagnosis scheme utilizes on-line approximators and adaptive nonlinear filtering techniques to obtain estimates of the fault functions. Robustness with respect to modeling uncertainties, fault sensitivity and stability properties of the learning scheme are rigorously derived and the theoretical results are illustrated by a simulation example of a fourth-order satellite model