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Robust Fault Detection by Means of Echo State Neural Network

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Advanced and Intelligent Computations in Diagnosis and Control

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 386))

Abstract

This paper deals with the application of Echo State Network (ESN) model to robust fault diagnosis of the Twin Rotor Aero-Dynamical System (TRAS) through modeling the uncertainty of the neural model with the so-called Model Error Modeling method (MEM). The work describes the modeling process of the plant and scenarios in which the system is under influence of the unknown fault. In such fault scenarios the ESN model together with MEM are used to form the uncertainty bands. If the system output exceeds the uncertain region the fault occurrence is signalized. All data used in experiments are collected from the TRAS through the Matlab/Simulink environment.

This work was supported by the National Science Center in Poland under the grant UMO-2012/07/N/ST7/03316.

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Authors and Affiliations

  1. Institute of Control and Computation Engineering, University of Zielona Góra, ul. Podgórna 50, 65-246, Zielona Góra, Poland

    Andrzej Czajkowski & Krzysztof Patan

Authors
  1. Andrzej Czajkowski
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  2. Krzysztof Patan
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Correspondence to Andrzej Czajkowski .

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Editors and Affiliations

  1. Faculty of Electronics, Telecommunications and Informatics, Gdańsk University of Technology, Gdańsk, Poland

    Zdzisław Kowalczuk

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Czajkowski, A., Patan, K. (2016). Robust Fault Detection by Means of Echo State Neural Network. In: Kowalczuk, Z. (eds) Advanced and Intelligent Computations in Diagnosis and Control. Advances in Intelligent Systems and Computing, vol 386. Springer, Cham. https://doi.org/10.1007/978-3-319-23180-8_25

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  • DOI: https://doi.org/10.1007/978-3-319-23180-8_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23179-2

  • Online ISBN: 978-3-319-23180-8

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