Automatica (Journal of IFAC)
Generalizations of singular optimal control theory
Automatica (Journal of IFAC)
Brief paper: LMI-based sensor fault diagnosis for nonlinear Lipschitz systems
Automatica (Journal of IFAC)
On fault detection in linear discrete-time, periodic, and sampled-data systems
Journal of Control Science and Engineering - Robustness Issues in Fault Diagnosis and Fault Tolerant Control
Optimal fault-detection filtering for non-Gaussian systems via output PDFs
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Fault detection for NARMAX stochastic systems using entropy optimization principle
CCDC'09 Proceedings of the 21st annual international conference on Chinese Control and Decision Conference
Optimal statistical fault detection with nuisance parameters
Automatica (Journal of IFAC)
A method for quantitative fault diagnosability analysis of stochastic linear descriptor models
Automatica (Journal of IFAC)
Hi-index | 22.15 |
A fault detection and identification algorithm, called optimal stochastic fault detection filter, is determined. The objective of the filter is to detect a single fault, called the target fault, and block other faults, called the nuisance faults, in the presence of the process and sensor noises. The filter is derived by maximizing the transmission from the target fault to the projected output error while minimizing the transmission from the nuisance faults. Therefore, the residual is affected primarily by the target fault and minimally by the nuisance faults. The transmission from the process and sensor noises is also minimized so that the filter is robust with respect to these disturbances. It is shown that the filter recovers the geometric structure of the unknown input observer in the limit where the weighting on the nuisance fault transmission goes to infinity. Further, the asymptotic behavior of the filter near the limit is determined by using a perturbation method. Filter designs can be obtained for both time-invariant and time-varying systems.