On Automatic Fault Diagnosis in Wind Turbine Condition Monitoring

Abstract

Automatic Diagnosis is a novel algorithm developed by Brüel & Kjær Vibro to monitor various failure modes in wind turbines continuously. The algorithm aims at reducing false positives and false negatives, and most importantly, at eliminating the burden of diagnosis by human. Vibration analysis in the order domain using angular resampling helps to deal with speed variation in the vibration signal. The algorithm subsequently identifies prominent peaks in the order powerspectrum, labels these peaks, and then monitor the trends of any families of sidebands and harmonics. In this paper, we compare the results of Automatic Diagnosis with physical inspections using data from wind turbines monitored by Brüel & Kjær Vibro. Results show that Automatic Diagnosis can accurately detect the faults, as confirmed by physical inspections on the turbines. Diagnosing machine condition using high-performance computational infrastructure, instead of using a human, could hugely cut the cost of simultaneously monitoring many wind turbines.