Acoustic condition monitoring of wind turbines: tip faults

As a significant and growing source of the world’s energy, wind turbine reliability is becoming a major concern. At least two fault detection techniques for condition monitoring of wind turbine blades have been reported in early literature, i.e. acoustic emissions and optical strain sensors. These require off-site measurement. The work presented here offers an alternative non-contact fault detection method based on the noise emission from the turbine during operation. An investigation has been carried out on a micro wind turbine under laboratory conditions. 4 severity levels for a fault have been planted in the form of added weight at the tip of one blade to simulate inhomogeneous debris or ice build up. Acoustic data is obtained at a single microphone placed in front of the rotor. Two prediction methods have been developed and tested on real data: one based on a single feature – rotational frequency spectral magnitude; and another based on a fuzzy logic interference using two inputs spectral peak and rotational peak variation with time. Results show that the single spectral peak feature can be used to determine fault severity in ranges. The implementation of the fuzzy logic using a further input feature is shown to significantly improve the detection accuracy.