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Robust fault diagnosis based on adaptive estimation and set-membership computations

Abstract : The proposed fault diagnosis scheme relies on a residual generation based on an adaptive observer covering linear time varying (LTV), linear parameter varying (LPV), and state-affine non-linear systems, all with bounded uncertainties. A residual evaluation is then performed by set-membership computations based on zonotopes (polytopes defined as the image of a hypercube by a linear application). The main advantage of the approach is its rigorous computation of the propagation of pre-specified modelling uncertainty bounds. Within the assumed uncertainty bounds, fault detection is guaranteed to be free of false alarm, while not being too much conservative, as illustrated on the model of a satellite.
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https://hal.inria.fr/hal-00854909
Contributor : Qinghua Zhang <>
Submitted on : Wednesday, August 28, 2013 - 1:09:47 PM
Last modification on : Friday, May 25, 2018 - 12:02:05 PM

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Christophe Combastel, Qinghua Zhang. Robust fault diagnosis based on adaptive estimation and set-membership computations. 6th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes (SAFEPROCESS), Aug 2006, Beijing, China. pp.1204-1209, ⟨10.3182/20060829-4-CN-2909.00201⟩. ⟨hal-00854909⟩

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