Learning deep representation of imbalanced SCADA data for fault detection of wind turbines

Longting Chen; Guanghua Xu; Qing Zhang; Xun Zhang

doi:10.1016/j.measurement.2019.03.029

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Learning deep representation of imbalanced SCADA data for fault detection of wind turbines

Longting Chen Guanghua Xu Qing Zhang Xun Zhang

Year: 2019 Journal: Measurement Vol: 139 Pages: 370-379 Publisher: Elsevier BV

DOI: 10.1016/j.measurement.2019.03.029

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Keywords:

SCADA Fault (geology) Margin (machine learning) Computer science Wind power Artificial intelligence Representation (politics) Generalization Process (computing) Data mining Benchmark (surveying) Extreme learning machine Artificial neural network Machine learning Engineering Mathematics

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Machine Fault Diagnosis Techniques

Physical Sciences → Engineering → Control and Systems Engineering

Oil and Gas Production Techniques

Physical Sciences → Engineering → Ocean Engineering

Electricity Theft Detection Techniques

Physical Sciences → Engineering → Electrical and Electronic Engineering

Learning deep representation of imbalanced SCADA data for fault detection of wind turbines

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