Representational Learning for Fault Diagnosis of Wind Turbine Equipment: A Multi-Layered Extreme Learning Machines Approach

Representational Learning for Fault Diagnosis of Wind Turbine Equipment: A Multi-Layered Extreme Learning Machines Approach

Reliable and quick response fault diagnosis is crucial for the wind turbine generator system (WTGS) to avoid unplanned interruption and to reduce the maintenance cost. However, the conditional data generated from WTGS operating in a tough environment is always dynamical and high-dimensional. To addr...

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Bibliographic Details
Main Authors: Zhi-Xin Yang, Xian-Bo Wang, Jian-Hua Zhong
Format: Article
Language:English
Published: MDPI AG 2016-05-01
Series:Energies
Subjects:
fault diagnosis
wind turbine
classification
extreme learning machines (ELM)
autoencoder (AE)
Online Access:http://www.mdpi.com/1996-1073/9/6/379

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http://www.mdpi.com/1996-1073/9/6/379

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