Data–Driven Techniques for the Fault Diagnosis of a Wind Turbine Benchmark Cover

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Data–Driven Techniques for the Fault Diagnosis of a Wind Turbine Benchmark

International Journal of Applied Mathematics and Computer Science

Volume 28 (2018): Issue 2 (June 2018)

By: Silvio Simani, Saverio Farsoni and Paolo Castaldi

Open Access

|Jun 2018

Authors

Silvio Simani

silvio.simani@unife.it

Department of Engineering University of Ferrara, Via Saragat 1/E, 44124, Ferrara, Italy

Saverio Farsoni

saverio.farsoni@unife.it

Department of Engineering University of Ferrara, Via Saragat 1/E, 44124, Ferrara, Italy

Paolo Castaldi

paolo.castaldi@unibo.it

Department of Electronics, Computer Science and Systems University of Bologna, Via Fontanelle 40, 47100, Forlì, Italy

DOI: https://doi.org/10.2478/amcs-2018-0018 | Journal eISSN: 2083-8492 | Journal ISSN: 1641-876X

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Language: English

Page range: 247 - 268

Submitted on: Mar 15, 2017

Accepted on: Jan 29, 2018

Published on: Jun 29, 2018

Published by: University of Zielona Góra

In partnership with: Paradigm Publishing Services

Publication frequency: 4 issues per year

Keywords:

fault diagnosis,

analytical redundancy,

neural networks,

residual generators,

fault estimation,

wind turbine benchmark.

Related subjects:

Applied mathematics

© 2018 Silvio Simani, Saverio Farsoni, Paolo Castaldi, published by University of Zielona Góra
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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