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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: 644266260
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: 385 | Views: 2,745

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.

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[1] A neural network-based robust unknown input observer design: Application to wind turbine, Witczak, Piotr, Patan, Krzysztof, Witczak, Marcin, Puig, Vincenc, Korbicz, Jozef, IFAC-PapersOnLine, ISSN 2405-8963, Issue 21, Volume 48, 2015.
Digital Object Identifier: 10.1016/j.ifacol.2015.09.538

[2] A Combined Model-based and Intelligent Method for Small Fault Detection and Isolation of Actuators, Cheng, Yao, Wang, Rixin, Xu, Minqiang, IEEE Transactions on Industrial Electronics, ISSN 0278-0046, 2015.
Digital Object Identifier: 10.1109/TIE.2015.2499722

[3] Actuator fault diagnosis and fault-tolerant control: Application to the quadruple-tank process, Buciakowski, Mariusz, de Rozprza-Faygel, Michał, Ochałek, Joanna, Witczak, Marcin, Journal of Physics: Conference Series, ISSN 1742-6588, Issue 8, Volume 570, 2014.
Digital Object Identifier: 10.1088/1742-6596/570/8/082002

[4] Towards Robust Neural-Network-Based Sensor and Actuator Fault Diagnosis: Application to a Tunnel Furnace, Witczak, Marcin, Mrugalski, Marcin, Korbicz, Józef, Neural Processing Letters, ISSN 1370-4621, Issue 1, Volume 42, 2015.
Digital Object Identifier: 10.1007/s11063-014-9387-0

[5] A fault-tolerant control strategy for non-linear discrete-time systems: application to the twin-rotor system, Witczak, Marcin, Puig, Vicenç, de Oca, Saúl Montes, International Journal of Control, ISSN 0020-7179, Issue 10, Volume 86, 2013.
Digital Object Identifier: 10.1080/00207179.2013.796592

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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania

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