Publication
Fault detection for uncertain LPV systems using probabilistic set-membership parity relation
Journal Article (2020)
Journal
Journal of Process Control
Pages
27-36
Volume
87
Doc link
http://dx.doi.org/10.1016/j.jprocont.2019.12.010
File
Authors
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Wan, Yiming
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Puig Cayuela, Vicenç
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Ocampo Martínez, Carlos A.
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Wang, Ye
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Harinath, Eranda
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Braatz, Richard
Projects associated
Abstract
This paper considers fault detection of uncertain linear parameter varying systems that have polynomial dependence on parametric uncertainties. A conventional set-membership (SM) approach is able to ensure zero false alarm rate (FAR) by using conservative threshold sets, but usually results in a high missed detection rate (MDR) due to equally treating all uncertainty realizations without distinguishing between high and low probability of occurrence. To address this limitation, a probabilistic SM parity relation approach is proposed to exploit probabilistic information on the parametric uncertainties, which results in a reduced MDR by admitting an acceptable FAR. The parity relation is first polynomially parameterized with respect to uncertain parameters. Then, Gaussian mixtures are adopted to efficiently compute uncertainty propagation from stochastic uncertainties to the residual distribution. To achieve an acceptable FAR, a non-convex confidence set of residuals { represented by a union of ellipsoids is determined for the consistency test. The effectiveness of the proposed approach is illustrated using a continuous stirred tank reactor example including performance comparisons with a deterministic zonotope-based method.
Categories
automation, control theory, optimisation.
Author keywords
Fault detection, linear parameter varying systems, probabilistic parametric uncertainties, parity relation, set membership approach
Scientific reference
Y. Wan, V. Puig, C. Ocampo-Martínez, Y. Wang, E. Harinath and R. Braatz. Fault detection for uncertain LPV systems using probabilistic set-membership parity relation. Journal of Process Control, 87: 27-36, 2020.
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