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An efficient FDIR strategy on nonlinear processes of self-validating multifunctional sensors

An efficient FDIR strategy on nonlinear processes of self-validating multifunctional sensors PurposeThis paper aims to enhance the reliability of self-validating multifunctional sensors.Design/methodology/approachAn effective fault detection, isolation and data recovery (FDIR) strategy by using kernel principal component analysis (KPCA) coupled with gray bootstrap and fault reconstruction methods.FindingsThe proposed FDIR strategy is able to the address fault detection, isolation and data recovery problem of self-validating multifunctional sensors efficiently.Originality/valueA KPCA-based model which can overcome the limitation of existing linear-based models is used to achieve the fault detection task. By using gray bootstrap method, the position of all faulty sensitive units can be calculated even under the multiple faults situation. A reconstruction-based contribution method is adopted to evaluate the amplitudes of the fault signals, and the fault-free output of the faulty sensitive units can be used to replace the fault output. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Sensor Review Emerald Publishing

An efficient FDIR strategy on nonlinear processes of self-validating multifunctional sensors

Sensor Review , Volume 37 (3): 14 – Jun 19, 2017

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Publisher
Emerald Publishing
Copyright
Copyright © Emerald Group Publishing Limited
ISSN
0260-2288
DOI
10.1108/SR-08-2016-0138
Publisher site
See Article on Publisher Site

Abstract

PurposeThis paper aims to enhance the reliability of self-validating multifunctional sensors.Design/methodology/approachAn effective fault detection, isolation and data recovery (FDIR) strategy by using kernel principal component analysis (KPCA) coupled with gray bootstrap and fault reconstruction methods.FindingsThe proposed FDIR strategy is able to the address fault detection, isolation and data recovery problem of self-validating multifunctional sensors efficiently.Originality/valueA KPCA-based model which can overcome the limitation of existing linear-based models is used to achieve the fault detection task. By using gray bootstrap method, the position of all faulty sensitive units can be calculated even under the multiple faults situation. A reconstruction-based contribution method is adopted to evaluate the amplitudes of the fault signals, and the fault-free output of the faulty sensitive units can be used to replace the fault output.

Journal

Sensor ReviewEmerald Publishing

Published: Jun 19, 2017

References