On-Line Fault Detection in Wind Turbine Transmission System using Adaptive Filter and Robust Statistical Features

Ruoyu Li and Mark Frogley
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Publication Issue: 
Special Issue Wind Turbine PHM
Submission Type: 
Full Paper
ijphm_13_019.pdf677.47 KBJuly 18, 2013 - 10:23pm

To reduce the maintenance cost, avoid catastrophic failure, and improve the wind transmission system reliability, online condition monitoring system is critical important. In the real applications, many rotating mechanical faults, such as bearing surface defect, gear tooth crack, chipped gear tooth and so on generate impulsive signals. When there are these types of faults developing inside rotating machinery, each time the rotating components pass over the damage point, an impact force could be generated. The impact force will cause a ringing of the support structure at the structural natural frequency. By effectively detecting those periodic impulse signals, one group of rotating machine faults could be detected and diagnosed. However, in real wind turbine operations, impulsive fault signals are usually relatively weak to the background noise and vibration signals generated from other healthy components, such as shaft, blades, gears and so on. Moreover, wind turbine transmission systems work under dynamic operating conditions. This will further increase the difficulties in fault detection and diagnostics. Therefore, developing advanced signal processing methods to enhance the impulsive signals is in great needs.
In this paper, an adaptive filtering technique will be applied for enhancing the fault impulse signals-to-noise ratio in wind turbine gear transmission systems. Multiple statistical features designed to quantify the impulsive signals of the processed signal are extracted for bearing fault detection. The multiple dimensional features are then transformed into one dimensional feature. A minimum error rate classifier will be designed based on the compressed feature to identify the gear transmission system with defect. Real wind turbine vibration signals will be used to demonstrate the effectiveness of the presented methodology.

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Submission Keywords: 
fault detection
fault diagnosis
condition monitoring
Adaptive filtering
Gear transmission system
Statistical features
Pattern classification
Wind turbine transmission system
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
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