Wind Turbine Gearbox Condition Monitoring with AAKR and Moving Window Statistic Methods
AbstractCondition Monitoring (CM) of wind turbines can greatly reduce the maintenance costs for wind farms, especially for offshore wind farms. A new condition monitoring method for a wind turbine gearbox using temperature trend analysis is proposed. Autoassociative Kernel Regression (AAKR) is used to construct the normal behavior model of the gearbox temperature. With a proper construction of the memory matrix, the AAKR model can cover the normal working space for the gearbox. When the gearbox has an incipient failure, the residuals between AAKR model estimates and the measurement temperature will become significant. A moving window statistical method is used to detect the changes of the residual mean value and standard deviation in a timely manner. When one of these parameters exceeds predefined thresholds, an incipient failure is flagged. In order to simulate the gearbox fault, manual temperature drift is added to the initial Supervisory Control and Data Acquisitions (SCADA) data. Analysis of simulated gearbox failures shows that the new condition monitoring method is effective. View Full-Text
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Guo, P.; Bai, N. Wind Turbine Gearbox Condition Monitoring with AAKR and Moving Window Statistic Methods. Energies 2011, 4, 2077-2093.
Guo P, Bai N. Wind Turbine Gearbox Condition Monitoring with AAKR and Moving Window Statistic Methods. Energies. 2011; 4(11):2077-2093.Chicago/Turabian Style
Guo, Peng; Bai, Nan. 2011. "Wind Turbine Gearbox Condition Monitoring with AAKR and Moving Window Statistic Methods." Energies 4, no. 11: 2077-2093.