Wind Turbine Gearbox Condition Monitoring with AAKR and Moving Window Statistic Methods
نویسنده
چکیده
Condition 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.
منابع مشابه
Managing Gearbox Failure
The wind industry has had a chronic problem with the reliability of its gearboxes [1], [2]. Experience has shown that premature gearbox failure is a leading maintenance cost driver that can easily consume the profit margin from a wind turbine operation. Condition monitoring is already understood to have potential to mitigate this risk by managing gearbox maintenance through the promises of reli...
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