Sound and vibration-based pattern recognition for wind turbines driving mechanisms

This paper proposes a pattern recognition approach for a Fault Detection and Diagnosis (FDD) system based on the wavelet and the fast Fourier transform. Both techniques are developed in an experimental set that simulates the driving mechanisms housed in the nacelle of a wind turbine (WT) with results being validated in a real wind farm. After a first separate approach of the vibration harmonics and the sound energy, the root mean square error (RMSE) is used to fuse the data into a common pattern. The pattern reveals accurate information for unstable features (e.g. the case of the sound) related to misalignments among other failures. Comparing the experiments with the pattern, it is observed that the pattern is often close to the induced failures with minor exceptions. Relations among all the measured points are also found. The usefulness of the findings lies in the possibility of monitoring inaccessible devices considering this relation. Cost savings based on the strategic placement of the sensors can be intended too. The FDD will ensure the implementation of predictive actions before the occurrence of a catastrophic failure in an area where there is an ongoing challenge for being competitive.