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Automated Fault Diagnosis is Nonlinear Multivariable
Trunov, A. B.
Polycarpou, M. M.
Article from Journal - ilmiah internasional
IEEE Transactions on Neural Networks vol. 11 no. 1 (2000)
Perpustakaan Pusat (Semanggi)
1 (dapat dipinjam: 0)
Lihat Detail Induk
The paper presents a robust fault diagnosis scheme for detecting and approximating state and output faults occurring in a class of nonlinear multi input - multi output 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 non linear 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.
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