Online wind turbine fault detection through automated SCADA data analysis

By A. Zaher, S. Mcarthur, David Infield, Y. Patel. 2009

479 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research, Political Science and International Relations

View Paper: https://www.semanticscholar.org/paper/0cadab4cb8f29f17da9942ec4afbf893e79f5cd0

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In Artificial Intelligence

In Management Science and Operations Research

In Political Science and International Relations

Citations in the corpus

Wind Turbine Gearbox Failure Identification With Deep Neural Networks (2017, unknown): “occurrences and minimize unscheduled downtime [3].”
A Survey on Wind Turbine Condition Monitoring and Fault Diagnosis—Part I: Components and Subsystems (2015, background): “Winding faults will also cause an increase in the winding temperature [57].”
A Survey on Wind Turbine Condition Monitoring and Fault Diagnosis—Part II: Signals and Signal Processing Methods (2015, methodology): “, mode, location, and severity) of different WT faults from the input signals containing the information of the faults, which can be raw signals [77], [80], [81], conditioned signals [6], [82], or features extracted from raw and conditioned signals [3], [83].”
An Artificial Neural Network Approach for Early Fault Detection of Gearbox Bearings (2015, methodology): “In [10], the anomaly is detected based on increasing error value in relation to a predefined confidence interval and in [11] the anomaly is detected based on increase in the duration and frequency of errors.”
Comparative analysis of neural network and regression based condition monitoring approaches for wind turbine fault detection (2011, unknown): “They were able to detect incipient problems 6 month (Zaher et al.) and 2 days (Sanz-Bobi et al.) before the actual failure [1].”
On Accurate and Reliable Anomaly Detection for Gas Turbine Combustors: A Deep Learning Approach (2015, methodology): “…been actively applied to different PHM applications including: aircraft engine fault detection [Tolani et al. (2006)], wind turbine fault detection [Zaher et al. (2009)], locomotive engine fault detection [Xue & Yan (2007)], marine gas turbine engine [Ogbonnaya et al. (2012)], and combined cycle…”
Machine learning methods for wind turbine condition monitoring: A review (2019, background): “This type of sys-tem collects basic informationwith the use of sensors placed on key wind turbine components (e.g. bearing vibration, temperature, phase currents, wind speed, etc.) [30] [31].”