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L. Wang, “Support Vector Machines: Theory and Applications,” Springer, New York, 2010.

has been cited by the following article:

  • TITLE: Construction of Wind Turbine Bearing Vibration Monitoring and Performance Assessment System

    AUTHORS: Feng-Tai Wu, Chun-Chieh Wang, Jui-Hung Liu, Chia-Ming Chang, Ya-Ping Lee

    KEYWORDS: Signal Processing; Feature Extraction; Performance Assessment; Performance Prediction; Fault Diagnosis

    JOURNAL NAME: Journal of Signal and Information Processing, Vol.4 No.4, November 18, 2013

    ABSTRACT: 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.