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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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  3/2012 - 10


State-Space GMDH Neural Networks for Actuator Robust Fault Diagnosis

MRUGALSKI, M. See more information about MRUGALSKI, M. on SCOPUS See more information about MRUGALSKI, M. on IEEExplore See more information about MRUGALSKI, M. on Web of Science, WITCZAK, M. See more information about WITCZAK, M. on SCOPUS See more information about WITCZAK, M. on SCOPUS See more information about WITCZAK, M. on Web of Science
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Download PDF pdficon (1,050 KB) | Citation | Downloads: 531 | Views: 4,369

Author keywords
fault diagnosis, robustness, actuators, neural networks, system identification

References keywords
fault(14), control(12), systems(11), neural(10), witczak(7), networks(7), estimation(7), mrugalski(6), korbicz(6), gmdh(6)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2012-08-31
Volume 12, Issue 3, Year 2012, On page(s): 65 - 72
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2012.03010
Web of Science Accession Number: 000308290500010
SCOPUS ID: 84865856659

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Most fault diagnosis methods focus on the fault detection of the system or sensors and do not take into account the problem of the fault detection and isolation of the actuators, which are an important part of the contemporary industrial systems. To solve such a problem, the system outputs and inputs estimator based on a dynamic Group Method of Data Handling neural network in the state-space representation is proposed. In particular, the methodology of the adaptive thresholds calculation for system inputs and outputs is presented. The approach is based on the application of the Unscented Kalman Filter and Unknown Input Filter is presented. This result enables performing robust fault detection and isolation of the actuators. The final part of the paper presents an application study, which confirms the effectiveness of the proposed approach.

References | Cited By  «-- Click to see who has cited this paper

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References Weight

Web of Science® Citations for all references: 4,012 TCR
SCOPUS® Citations for all references: 5,635 TCR

Web of Science® Average Citations per reference: 129 ACR
SCOPUS® Average Citations per reference: 182 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2021-05-09 12:28 in 103 seconds.

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Disclaimer: All queries to the respective databases were made by using the DOI record of every reference (where available). Due to technical problems beyond our control, the information is not always accurate. Please use the CrossRef link to visit the respective publisher site.

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