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2017 | OriginalPaper | Buchkapitel

Neural Network-Based Simultaneous Estimation of Actuator and Sensor Faults

verfasst von : Marcin Pazera, Marcin Witczak, Marcin Mrugalski

Erschienen in: Advances in Computational Intelligence

Verlag: Springer International Publishing

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Abstract

The paper is devoted to the problem of a neural network-based robust simultaneous actuator and sensor faults estimator design for the purpose of the Fault Diagnosis (FD) of non-linear systems. In particular, the methodology of designing a neural network-based \(\mathcal {H_\infty }\) fault estimator is developed. The main novelty of the approach is associated with possibly simultaneous sensor and actuator faults. For this purpose, a Linear Parameter Varying (LPV) description of a Recurrent Neural Network (RNN) is exploited. The proposed approach guaranties a predefined disturbance attenuation level and convergence of the estimator. The final part of the paper presents an illustrative example concerning the application of the proposed approach to the multi-tank system fault diagnosis.

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Titel: Neural Network-Based Simultaneous Estimation of Actuator and Sensor Faults
verfasst von: Marcin Pazera
Marcin Witczak
Marcin Mrugalski
Verlag: Springer International Publishing
Buch: Advances in Computational Intelligence
Print ISBN: 978-3-319-59152-0

Electronic ISBN: 978-3-319-59153-7

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-59153-7_27

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