On-Line Fault Detection in Wind Turbine Transmission System using Adaptive Filter and Robust Statistical Features
To reduce the maintenance cost, avoid catastrophic failure, and improve the wind transmission system reliability, online condition monitoring system is important. Developing effective online fault detection methodology is important. In this paper, an adaptive filtering technique is applied for enhancing the fault impulse signals-to-noise ratio in wind turbine gear transmission systems. Multiple statistical features designed to quantify the impulse signals of the processed signal are extracted for rotating machine fault detection. The multiple dimensional features are then transformed into one dimensional feature. A minimum error rate classifier will be designed based on the transformed one dimensional feature to identify the gear transmission system with defect. Vibration signals collected from wind turbines in the real operation will be used to demonstrate the effectiveness of the presented methodology.
condition monitoring, fault detection, fault diagnosis, Adaptive filtering, Gear transmission system, Statistical features, Pattern classification, Wind turbine transmission system
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