Automated operational modal analysis (AOMA) is a common standard for unsupervised, data-driven, and outputonly 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.
Automated Operational Modal Analysis of a Helicopter Blade with a Density-Based Cluster Algorithm / Sibille, Luigi; Civera, Marco; ZANOTTI FRAGONARA, Luca; Ceravolo, Rosario. - In: AIAA JOURNAL. - ISSN 0001-1452. - 61:3(2023), pp. 1411-1427. [10.2514/1.J062084]
Automated Operational Modal Analysis of a Helicopter Blade with a Density-Based Cluster Algorithm
Luigi Sibille;Marco Civera;Luca Zanotti Fragonara;Rosario Ceravolo
2023
Abstract
Automated operational modal analysis (AOMA) is a common standard for unsupervised, data-driven, and outputonly 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.File | Dimensione | Formato | |
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https://hdl.handle.net/11583/2974414