Improved damage detectability in a wind turbine blade by optimal selection of vibration signal correlation coefficients

The central message of this article is that for robust and efficient damage detection the damage sensitive features should be selected optimally in a systematic way such that only these features that contribute the most to damage detectability be retained. Furthermore, suitable transformations of the original features may also enhance damage detectability. We explore these principles using data from a wind turbine blade. Several damage extent scenarios are introduced non-destructively. Partial autocorrelation coefficients are proposed as vibration-based damage sensitive features. Scores calculated with principal component analysis of partial autocorrelation coefficients are the transformed damage sensitive features. Statistical distances between the damage sensitive feature subsets estimated from the healthy and a reference damage state are calculated with respect to a statistical threshold as a measure of optimality. The fast forward method and a genetic algorithm are used to optimize the detectability of damage by damage sensitive feature selection. The comparison between the two methods points out that fast forward offers a comparable performance at a lower computational cost. The classifiers based on the optimal features are tested on data from several previously unseen healthy and damaged cases and across a range of statistical detection thresholds. It is demonstrated that the selected principal component analysis scores of the partial autocorrelation coefficients are superior compared to the initial features and allow identifying small damage confidently.