Probabilistic fault diagnostics using ensemble time-varying decision tree learning

descriptionPublicationkeyboard_double_arrow_right Presentation , Other literature type , Conference object 01 Jan 2019Publisher:ZenodoJournal:EMI 2019 - Engineering Mechanics Institute ConferenceFunded by:EC | WINDMIL, EC | WINDMIL RT-DT

Authors: Abdallah, Imad; Dertimanis, Vasilis; Chatzi, Eleni;

doi: 10.5281/zenodo.3474633 , 10.13140/rg.2.2.13226.36805 , 10.5281/zenodo.3474632

Probabilistic fault diagnostics using ensemble time-varying decision tree learning

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Abstract

Probabilistic fault diagnostics using ensemble time-varying decision tree learning on wind turbines. Simulations using Simulink and an embedded FAST aeroelastic model of the 5MW reference wind turbine. Error cases: (1) No Error, (2) Yaw Error, Corrected, (3) Yaw Error, yaw actuator stuck, (4) Pitch angle sensor, stuck at constant value of 5 deg.

Related Organizations

ETH Zurich
Switzerland

Keywords

Fault diagnostics, Machine learning, Wind turbines, Decision tree

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