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Naji, M. |
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Motta, Antonella |
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Aletan, Dirar |
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Mohamed, Tarek |
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Ertürk, Emre |
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Taccardi, Nicola |
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Kononenko, Denys |
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Petrov, R. H. | Madrid |
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Alshaaer, Mazen | Brussels |
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Bih, L. |
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Casati, R. |
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Muller, Hermance |
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Kočí, Jan | Prague |
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Šuljagić, Marija |
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Kalteremidou, Kalliopi-Artemi | Brussels |
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Azam, Siraj |
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Ospanova, Alyiya |
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Blanpain, Bart |
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Ali, M. A. |
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Popa, V. |
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Rančić, M. |
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Ollier, Nadège |
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Azevedo, Nuno Monteiro |
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Landes, Michael |
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Rignanese, Gian-Marco |
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Papaelias, Mayorkinos
University of Birmingham
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (5/5 displayed)
- 2020Damage monitoring of surface treated steel under severe rolling contact loading conditionscitations
- 2020Perspectives on railway axle bearing condition monitoringcitations
- 2020Utilisation of ensemble empirical mode decomposition in conjunction with cyclostationary technique for wind turbine gearbox fault detectioncitations
- 2018Quantitative monitoring of brittle fatigue crack growth in railway steel using acoustic emissioncitations
- 2016Inspection and Structural Health Monitoring techniques for Concentrated Solar Power plantscitations
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article
Utilisation of ensemble empirical mode decomposition in conjunction with cyclostationary technique for wind turbine gearbox fault detection
Abstract
In this paper the application of cyclostationary signal processing in conjunction with Ensemble Empirical Mode Decomposition (EEMD) technique, on the fault diagnostics of wind turbine gearboxes is investigated and has been highlighted. It is shown that the EEMD technique together with cyclostationary analysis can be used to detect the damage in complex and non-linear systems such as wind turbine gearbox, where the vibration signals are modulated with carrier frequencies and are superimposed. In these situations when multiple faults alongside noisy environment are present together, the faults are not easily detectable by conventional signal processing techniques such as FFT and RMS.