On the use of mode shape curvatures for damage localization under varying environmental conditions
Im Viewer öffnen
Dertimanis, Vasilis K.
Chatzi, Eleni N.
Savoia, Marco N.
- Journal Article
Im Viewer öffnen
Rechte / LizenzCreative Commons Attribution 4.0 International
A novel damage localization method is introduced in this study, which exploits mode shape curvatures as damage features, while accounting for operational variability. The developed framework operates in an output‐only regime,that is, it does not assume availability of records from the influencing environmental/operational quantities but rather from response quantities alone. The introduced tool comprises 3 stages pertaining to training, validation, and diagnostics. During the training stage, a representation of the healthy, or baseline, structural state is acquired over varying operational conditions. A data matrix is formulated, whose individual columns correspond to mode shape curvatures at distinct operational conditions, and principal component analysis (PCA) is applied for extraction of the imprints of separate operational sources on these curvatures. To this end, a residual matrix between the original and the PCA mapped data is formed serving for statistical characterization of each mode. Subsequently, during the validation and diagnostics stages, the mode shape curvature matrices for the currently inspected structural state are assembled and the same PCA mapping is enforced. A typical hypothesis test and a corresponding damage index are then adopted in order to firstly detect damage, and to secondly localize damage, should this exist. The implementation of the proposed method in 2 numerical case studies confirms its effectiveness and the encouraging results suggest further investigation on operating structural systems Mehr anzeigen
Zeitschrift / SerieStructural Control and Health Monitoring
Seiten / Artikelnummer
Themadamage detection and localization; environmental and operational variability; mode shape curvature; principal component analysis; structural health monitoring