Data-driven design of fault detection and isolation systems subject to Hammerstein nonlinearity

This paper is concerned with data-driven design of fault detection and isolation (FDI) systems subject to Hammerstein nonlinearity, a static nonlinearity in the front of inputs. Specifically, the design of residual generation is then formulated as to solve a convex optimization problem by combining...

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Bibliographic Details
Published in2015 American Control Conference (ACC) pp. 214 - 219
Main Authors Yulei Wang, Bingzhao Gao, Hong Chen
Format Conference Proceeding
LanguageEnglish
Published American Automatic Control Council 01.07.2015
Subjects
Benchmark testing
Convex functions
Covariance matrices
Fault detection
Generators
Linear systems
Permanent magnet motors
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Summary:This paper is concerned with data-driven design of fault detection and isolation (FDI) systems subject to Hammerstein nonlinearity, a static nonlinearity in the front of inputs. Specifically, the design of residual generation is then formulated as to solve a convex optimization problem by combining ideas from the over-parameterization and least squares support vector machines (LS-SVMs), and thus provides residual signals directly from process data. To solve the multiply-outputs (MOs) problem, a modified approach is proposed by means of the so-called mixed block Hankel matrices. Sufficient conditions for the existence of a parity space are established and proved. A benchmark example is given to show the effectiveness of the proposed approach.
ISSN:0743-1619
DOI:10.1109/ACC.2015.7170738