Wind Turbines Condition Monitoring and Fault Identification using Acoustic Emission Testing Method

Abstract

Acoustic Emission (AE) technique can successfully applied for condition monitoring of low speed rotating components such as rolling bearing and gearbox of Wind Turbines. This technique is able to detect very small energy released rates from incipient defect in a very early stage. Wide range of signal processing methods can be apply for diagnosing faults and fatigues in AE spectrums and the changes in wave forms are very significant to recognize the failures. Condition monitoring and Fault identification (CMFI) of wind turbine health using automated failure detection algorithms can improve turbine reliability. AE testing is based condition monitoring system uses data already collected at the wind turbine controller. It is an effective way to monitor wind turbines for early warning of failures and performance issues. CMFI methods are classified into modelbased and signal-based methods. They can be implemented with or without the use of artificial intelligence. The object of this thesis is to design a model-based CMFI scheme for WTs, which can be used under normal operation conditions.