Variable-Structure Multiple-Model Approach to Fault Detection, Identification, and Estimation

A scheme is proposed to detect, identify, and estimate failures, including abrupt total, partial, and multiple failures, in a dynamic system. The new approach, named IM 3 L, is developed based on variable-structure multiple-model estimation, which allows to improve performance by online adaptation....

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Bibliographic Details
Published in:IEEE transactions on control systems technology 2008-09, Vol.16 (5), p.1029-1038
Main Authors: Ru, J., Rong Li, X.
Format: Article
Language:English
Subjects:
Adaptive estimation
Aircraft
Applied sciences
Computer science
control theory
systems
Control theory. Systems
Dynamical systems
Dynamics
Estimators
Exact sciences and technology
Failure
Fault detection
fault detection and identification
Fault diagnosis
Filters
hybrid estimation
Likelihood ratio
Mathematical model
Maximum likelihood detection
Maximum likelihood estimation
Modelling and identification
multiple-model
On-line systems
Redundancy
Robustness
State estimation
variable structure
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Description
Summary:A scheme is proposed to detect, identify, and estimate failures, including abrupt total, partial, and multiple failures, in a dynamic system. The new approach, named IM 3 L, is developed based on variable-structure multiple-model estimation, which allows to improve performance by online adaptation. It uses an interacting multiple model estimator for fault detection and identification but the maximum likelihood estimator for estimating the extent of failure. It provides an effective and integrated framework for fault detection, identification, and state estimation. For two aircraft examples, the proposed approach is evaluated and compared with hierarchical multiple-model approaches and a widely used single-model residual-based generalized likelihood ratio approach in terms of detection and estimation performance. The results show that the IM 3 L provides not only fast detection and proper identification, but also good estimation of the failure extent as well as robust state estimation.
ISSN:1063-6536
1558-0865
DOI:10.1109/TCST.2007.916318