Automated Operational Modal Analysis of a Helicopter Blade with a Density-Based Cluster Algorithm
Peer reviewed, Journal article
Published version
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https://hdl.handle.net/11250/3053725Utgivelsesdato
2022Metadata
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Sammendrag
Automated operational modal analysis (AOMA) is a common standard for unsupervised, data-driven, and output-only system identification, utilizing ambient vibrations as an environmental input source. However, conventional AOMA approaches apply the k
-means clustering algorithm (with k=2
) to discern possibly physical and certainly mathematical modes. That is not totally appropriate due to the intrinsic tendency of k
-means to produce similarly sized clusters, as well as its limitation to approximately normally distributed variables. Hence, a novel approach, based on the density-based clustering algorithm Density-Based Spatial Clustering of Applications with Noise (DBSCAN), is introduced here. Among other technical advantages, this enables to automatically detect and remove outliers. A data-driven strategy for the DBSCAN parameter selection is proposed as well, to make the whole procedure fully automated. This methodology is then validated on a case of aeronautical interest, an Airbus Helicopter H135 bearingless main rotor blade, and compared to more classic strategies for the same case study.