摘要：This study
is primary to develop relevant techniques for the bearing of wind turbine, such
as the intelligent monitoring system, the performance assessment, future trend
prediction and possible fault classification etc. The main technique of system
monitoring and diagnosis is divided into three algorithms, such as the performance
assessment, performance prediction and fault diagnosis, respectively. Among
them, the Logistic Regression (LR) is adopted to assess the bearing performance
condition, the Autoregressive Moving Average (ARMA) is adopted to predict the
future variation trend of bearing, and the Support Vector Machine (SVM) is
adopted to classify and diagnose the possible fault of bearing. Through
testing, this intelligent monitoring system can achieve real-time vibration
monitoring, current performance assessment, future performance trend prediction
and possible fault classification for the bearing of wind turbine. The monitor
and analysis data and knowledge not only can be used as the basis of predictive
maintenance, but also can be stored in the database for follow-up off-line
analysis and used as the reference for improvement of operation parameter and
wind turbine system design.
