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Sparse set membership identification of nonlinear functions and application to fault detection
Author(s) -
Novara Carlo
Publication year - 2016
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.2539
Subject(s) - interval (graph theory) , fault detection and isolation , fault (geology) , bounded function , identification (biology) , nonlinear system , function (biology) , set (abstract data type) , nonparametric statistics , computer science , mathematical optimization , class (philosophy) , wind power , noise (video) , algorithm , mathematics , engineering , artificial intelligence , statistics , mathematical analysis , botany , physics , combinatorics , quantum mechanics , evolutionary biology , seismology , electrical engineering , actuator , image (mathematics) , biology , programming language , geology
Summary The problem of approximating an unknown function from data and deriving reliable interval estimates is important in many fields of science and technology. In this paper, an algorithm is proposed to solve this problem, based on a sparsification technique and a nonparametric set membership analysis. Assuming that the noise affecting the data is bounded and the unknown function satisfies a mild regularity assumption, it is shown that the algorithm provides an approximation with suitable optimality properties, together with tight interval estimates. An innovative approach to fault detection, based on the derived interval estimates, is then proposed, overcoming some relevant problems proper of the ‘classical’ techniques. The approach is applied in a simulation study to solve the challenging problem of fault detection for a new class of wind energy generators, which uses kites to capture the power from high‐altitude winds. Copyright © 2015 John Wiley & Sons, Ltd.