Loading...
Loading...

Go to the content (press return)

Detection of structural changes through principal component analysis and multivariate statistical inference

Author
Pozo, F.; Arruga, I.; Mujica, L.E.; Ruiz, M.; Podivilova, E.
Type of activity
Journal article
Journal
Structural health monitoring: an international journal
Date of publication
2016-01-27
DOI
https://doi.org/10.1177/1475921715624504 Open in new window
Repository
http://hdl.handle.net/2117/87419 Open in new window
http://shm.sagepub.com/content/early/2016/01/26/1475921715624504.abstract Open in new window
URL
http://shm.sagepub.com/ Open in new window
Abstract
This article introduces a new methodology for the detection of structural changes using a statistical data-driven modeling approach by means of a distributed piezoelectric active sensor network at different actuation phases. The three main features that characterize the proposed methodology are (a) the nature of the data used in the test since vectors of principal component analysis projections are used instead of the entire measured response of the structure or the coefficients of an AutoRegres...
Citation
Pozo, F., Arruga, I., Mujica, L.E., Ruiz, M., Podivilova, E. Detection of structural changes through principal component analysis and multivariate statistical inference. "Structural health monitoring: an international journal", 27 Gener 2016.
Keywords
Damage detection, multivariate hypothesis testing, principal component analysis, structural health monitoring.
Group of research
CoDAlab - Control, Dynamics and Applications

Participants

Attachments