DEVELOPMENT OF AUTOMATIC HEALTH CONDITION ANALYZING/MONITORING SYSTEM ON WIND TURBINE BLADES

Abstract

In this study, we built up a total solution for blade surface diagnosis on a wind turbine. By capturing sound/noise from healthy blades through a conventional condenser microphone (1/4”130D20, PCB Piezotronics), we constructed standard characteristic curves for three wind speed intervals. Then, we used a MEMS microphone to replace the condenser microphone for signal capturing. After verifying the functionality of the microphone, the device was covered with membrane/mesh to eliminate wind noise/throb during sound recording and provide protection from dust and water. The membranes/meshes were specially chosen with high IP rating to resist the severe environment. Thereafter, we constructed an automatic diagnosis system ported from PC with MATLAB software to single ADLINK Technology® MCM-204 standalone Ethernet DAQ device. Analyzing and comparing the time-frequency diagram through Short-Time Fourier Transform (STFT) method and a self-developed algorithm on MATLAB software shows the intensity changing in the time-frequency diagram compared to the standard data (recorded by condenser microphone), whereas the spectrogram from the data recorded by MEMS microphone shows a similar characteristic pattern. These results show the possibility of constructing an unmanned, automatic analyzing/monitoring system for turbine monitoring with low component cost.