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An imbalance fault detection method based on data normalization and EMD for marine current turbines

Authors :
Tianhao Tang
Tianzhen Wang
Demba Diallo
Mohamed Benbouzid
Milu Zhang
Shanghai Maritime University
Laboratoire brestois de mécanique et des systèmes (LBMS)
École Nationale d'Ingénieurs de Brest (ENIB)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)
Laboratoire Génie électrique et électronique de Paris (GeePs)
Université Paris-Sud - Paris 11 (UP11)-Université Pierre et Marie Curie - Paris 6 (UPMC)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)
Source :
ISA Transactions, ISA Transactions, Elsevier, 2017, 68, pp.302-312. ⟨10.1016/j.isatra.2017.02.011⟩, ISA Transactions, 2017, 68, pp.302-312. ⟨10.1016/j.isatra.2017.02.011⟩
Publication Year :
2017
Publisher :
Elsevier BV, 2017.

Abstract

International audience; This paper proposes an imbalance fault detection method based on data normalization and Empirical Mode Decomposition (EMD) for variable speed direct-drive Marine Current Turbine (MCT) system. The method is based on the MCT stator current under the condition of wave and turbulence. The goal of this method is to extract blade imbalance fault feature, which is concealed by the supply frequency and the environment noise. First, a Generalized Likelihood Ratio Test (GLRT) detector is developed and the monitoring variable is selected by analyzing the relationship between the variables. Then, the selected monitoring variable is converted into a time series through data normalization, which makes the imbalance fault characteristic frequency into a constant. At the end, the monitoring variable is filtered out by EMD method to eliminate the effect of turbulence. The experiments show that the proposed method is robust against turbulence through comparing the different fault severities and the different turbulence intensities. Comparison with other methods, the experimental results indicate the feasibility and efficacy of the proposed method.

Details

ISSN :
00190578 and 18792022
Volume :
68
Database :
OpenAIRE
Journal :
ISA Transactions
Accession number :
edsair.doi.dedup.....d8bf9c8794478adc7da5af3a339340f0
Full Text :
https://doi.org/10.1016/j.isatra.2017.02.011