wind turbine gearbox condition monitoring with aakr and moving window statistic methods风力发电机齿轮箱状态监测与aakr统计方法和移动窗口.pdfVIP

Energies 2011, 4, 2077-2093; doi:10.3390/en4112077 OPEN ACCESS energies ISSN 1996-1073 /journal/energies Article Wind Turbine Gearbox Condition Monitoring with AAKR and Moving Window Statistic Methods Peng Guo and Nan Bai * School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China; E-Mail: huadiangp@163.com * Author to whom correspondence should be addressed; E-Mail: bainan66@163.com; Tel./Fax: +86-10-6177-2654. Received: 30 August 2011; in revised form: 24 October 2011 / Accepted: 21 November 2011 / Published: 23 November 2011 Abstract: 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 threshol